SlideShare a Scribd company logo
1 of 8
Download to read offline
Virtual Telemedicine using Natural Language Processing
                                                      Imran Sarwar Bajwa
                                               Department of Computer Science & IT
                                               The Islamia University of Bahawalpur
                                                      imran.sarwar@iub.edu.pk



                          Abstract                                  way of getting medical treatment at home. Health care
                                                                    facilities can be improved for a specific community:
Conventional telemedicine can be inept due to the existing time     children, old people, plague disease, etc. Telemedicine
constraints in response of the medical specialist. One major        can become moiré effective in emergency cases and areas
reason is that telemedicine based medical facilities are subject
                                                                    of natural disasters. Still, this is cost effective and
to the availability of the medical expert and telecommunication
facilities, when they are required. On the other hand,
                                                                    efficient way of providing high level and skilled medical
communication using telecommunication is only possible on           facilities to the people living in remote areas [2], who can
fixed and appointed time. Typically, the field of telemedicine      easily access the physicians and medical specialist.
exists in both medical and telecommunication areas to provide
medical facilities over long distance especially in remote areas.   1.1 Conventional Telemedicine
In this paper, a solution ‘virtual telemedicine’ is presented to
cope up with the problem of long time constraint that is faced in   Telemedicine typically works in two ways [3]: store and
conventional telemedicine. Virtual Telemedicine is the use of       forward method and real time method. Store and forward
telemedicine with the methods of artificial intelligence to over    method gathers patient’s medical information locally and
come the problems of telemedicine. Virtual medicine uses a          then patient query is emailed to a physician. Afterwards,
virtual physician that can treat patients anywhere, any time in     physician prescribes a treatment and then emails the
remote areas as well. Virtual telemedicine can be accessed          response of the medical query in 24 to 48 hours. On the
online as well.
                                                                    other hand, in real time telemedicine, video conferencing
Keywords – Telemedicine, Telecommunication for health,              and live data transmission methods are involved for
Information retrieval, Text Processing, Expert system               communication between patient and medical expert.
                                                                    UCD Health system [4] is one of the examples of video
                                                                    conference based health systems.
1. INTRODUCTION
Telecommunication is the most used technology all over
the world in current age and still establishing a long way.
This technology has made things to do in an easy and fast
manner. Now enhancements in technology have made our
thoughts to drag fields of life into advance technology.
From last few years, alphabet ‘e’ is being used with
almost everything i.e. e-mail, e-learning, e-commerce, e-
banking and e-services. The proposal of ‘e-health’ is still
new and asks for more development. Medical is the field
that is emerging continuously to make health facilities                   Figure-1.1: UCD Health system – Patient side [4]
more affective and facilitating. Telemedicine [1] is the
need of current age to provide health facilities in the
remote areas where medical experts, doctors and
physicians are not available. Telemedicine uses
telecommunication technology to provide medical
treatment and services. Telemedicine connects patients
with doctors where distance is a critical factor and
exchanges the information of diagnosis, treatment and
other health care activities.

Telemedicine becomes more significant if the patient is
far away from the medical experts and faces
transportation challenges. On the other hand it is helpful               Figure-1.2: UCD Health system – Physician side [4]
In store and forward method is quite approving solution         researchers who realized the importance and need of the
but it requires lot of time to get diagnostic results in        telemedicine based medical facilities. His study
return. Time constraint can be up to 24 to 48 hours. In         elaborated the use of store and forward method of
real time telemedicine, there are so many constraints that      medical information transformation. His work also
make its effective usability difficult. While in countries      emphasizes the need of efficient use of the resources to
like Pakistan [1] where video conferencing is a pricey          make the telemedicine based health care system more
client, real mode is not appropriate solution. Secondly,        effective and useful. Albert and Jason conducted two
high bandwidth is required for data transmission. On the        preliminary studies [5] in year 2007 to examine the
other hand, the availability of the medical expert is also      performance of remote display protocol (RDP) used in
required, when the patients need. Virtual telemedicine is       telemedicine systems. In first study, RDP was deployed
the process to provide the telemedicine features online         in a wide-area network [6] and in second one, the
using a virtual physician in place of the real doctor. Other    performance of RDP was analyzed over Wi-Fi [7]. They
famous telemedicine types are home telemedicine and             also presented a thin client based home telemedicine
individual telemedicine [3].                                    architecture that was providing remote training for
                                                                patients on broadband.
1.2. Virtual Telemedicine
                                                                Dena S. [9] discussed uses and benefits of telemedicine
As we have discussed in the previous section, that store        typically for rural areas in America. She presented that
and forward method is reasonably practicable but the            considerable technical, organizational, and financial
time constraint of store this method is not realistic. As       obstacles have kept the rural communities deprived of
some times due to the serious condition of the patient,         benefits of the technology. This paper focuses on these
he/she may not wait for up to 48 hours [11]. Some               issues and suggests a feasible solution for establishing
intelligent mechanism is required to improve the usability      successful rural telemedicine programs. DIABTel [10]
and affectivity of the conventional telemedicine process.       Telemedicine Service is another telemedicine based
An intelligent system is required that may provide              system that provides daily care to diabetic patients. Major
immediate response. In conventional telemedicine, an            concern of the research was to provide telemonitoring of
additional component is proposed in this research: virtual      patient's blood glucose data and also support remote care
physician. Virtual physician is a web-based application         from doctors to diabetic patients. Tayab D. [2] proposed a
that answers without delay the medical queries. To make         cost effective and multipurpose model of the
this facility more comprehensive, an additional                 telemedicine system. The proposed system had two major
functionality of consultation is also involved. In this         parts: a telemedicine unit for the patient side and another
facility, if the knowledgebase of the virtual physician         base unit for medical expert side. Major issue of
cannot answer a medical query an automatic email is sent        discussion was the use of high-speed network forms for
to a medical expert and the response of the query is            interconnectivity of the complete system. Dena Puskin,
updated in the knowledgebase for future queries.                Barbara and Stuart presented a framework [8] of a
                                                                telehealth system that was able to identify and understand
In this article, the section 2 presents the review of related   the interaction between telemedicine services.
work done be the various researchers in the field of            Exploration of health information technology [8] (HIT)
telemedicine and its applications in different areas of         applications on local, regional and national levels was the
healthcare. Section 3 highlights the architecture of the        major emphasis of this research.
designed medical expert system and the NLP based
algorithm that process the textual information. Section 4       UC Davis used a telepharmacy program in UCD Health
describes the implementation details and the section 5          System [4] that was based on a video conferencing. The
presents a case study to elaborate the use of the designed      author cites many challenges to telemedicine in the recent
system and the results of the performed experiments with        times i.e. system expertise, imprecise administration,
the analysis are also provided in later half of the same        contractual organization, etc. Tele-echocardiology [12] is
section.                                                        another field of major research in telemedicine. This field
                                                                of research deals with the real time diagnosis if heart
                                                                diseases without the support of in-house pediatric
2. LITERATURE REVIEW
                                                                cardiologists. The major emphasis of the research was to
Field of telemedicine is being proved the technology of         evaluate the impact of the telemedicine in providing the
the electronic age. Although the telemedicine was first         health care facilities to the cardiac patient in community
time used in 1959 but major development work was                hospitals where cardiac specialists are not available
initiated in this field for the last 8 to 10 years.             frequently.
Telemedicine has been used for the e-health solution of
                                                                In the recent times where wireless technologies are
diseases: diabetes, cardiac, trauma, and general physician
                                                                grasping their roots in other fields of life, at the same
related diseases. P. Douglas [11] was one of the earlier
time telemedicine is also getting benefits of it. An         transmit patient’s information to the medical expert. Still
advanced wireless sensor network (WSN) [] for health         there are important issues like accurate information
monitoring is introduced by G. Virone in DCS,                exchange, security, transmission bandwidth, protocols,
University of Virginia. The research presents a proposal     data sets etc.
‘smart healthcare’ with the benefits of low cost and ad-
hoc deployment of model sensors of for an improved
quality of health care. A. Diver [17] has recently
introduced his work to emphasize the significance of
image analysis as an additional support for assure the
modern telemedicine needs. A pilot study based on
twenty patients of trauma has been presented to highlight
the limited plastic surgery experience of a doctor in the
serious cases. Some outcomes of the work are
introduction of user-friendly technology, clinically
appropriate telemedicine applications, well trained and
professional telemedicine users, etc.


3. USED METHODOLOGY
Virtual telemedicine is replacing the physician in
telemedicine with a virtual physician. Telemedicine is
designed for remote and rural areas [12] whereas virtual
telemedicine can be used in both rural and urban areas. In
conventional telemedicine, there are simply two nodes:               Figure-3.2: A virtual telemedicine system
patient and doctor. Patient communicates with the doctor
through some telecommunication medium; telephone, e-         The time constraint of conventional telemedicine system
mail, internet, video-conferencing, etc. A simple            is typically longer. An idea of virtusal telemedicine has
representation of a conventional telemedicine system has     been presented to cover up this time constraint and make
been shown in figure 3.1.                                    telemedicine more effective and efficient. Virtual
                                                             telemedicine is the extension of conventional. A new
                                                             component ‘medical expert system’ has been deployed in
                                                             the conventional telemedicine system. This medical
                                                             expert system is a natural language processing based
                                                             expert system. In this research this expert system has
                                                             been named ‘Virtual Medical Expert System’.

                                                             3.1. Designed System Architecture

                                                             This virtual medical expert based system is shown in
                                                             figure 3.2. This system has robust ability of reading the
                                                             patient’s symptoms and immediately diagnosing the
                                                             disease and also prescribing the appropriate medication
                                                             for the patient. A natural language processing (NLP)
                                                             based medical expert system is the base of the proposed
                                                             health care system. The designed rule based expert
                                                             system has following major components [19].

                                                             a- Graphical User Interface
       Figure - 3.1: A simple telemedicine system            b- Medical Expert Knowledge base
Major issues that are concerned with the development of      c- Medical Inference engine
a conventional telemedicine system can be divided into       d- Medical Explanation Module
four categories [13]. First of all there is need of          a. Graphical User Interface
infrastructure that is based on hardware, software and
connectivity mechanism of multiple nodes (patient and        A graphical user interface is a facility for the user to
doctor). On the other hand basic medical equipment is        interact with the Expert system. A wizard of forms is
required at the patient end where a literate person can      used to get textual input from the user and then after
processing the textual information the output is shown to          •   Heuristic Knowledge is typically observed or
the user in the form of reports.                                       pragmatic knowledge. This type of knowledge is
                                                                       extracted from the factual knowledge i.e. “if patient
b- Medical Expert Knowledge base                                       has temperature then it can be chest infection”.
MEKB is an intelligent knowledge base that uses Markov             c. Medical Expert System
Logics (ML) to save domain knowledge. Markov Logic
is simple extension to first-order logic. In Markov Logic,         This is another very important part of the designed
each formula has an additional weight fixed with it [5], in        medical expert system. It is the brain of the medical
variation of first order logic. In ML, a formula's                 expert system. The major duty if this part is to make
associated weight reflects the strength of a constraint.           logical deductions based upon the extracted knowledge
The higher weight of a formula represents the greater the          from the medical expert knowledge base (MEKB). This
difference in log probability and it also satisfies the            inference engine not only makes decision but also
formula. Use of Markov Logic enables intelligent storage           extracts new information on the behalf of provided
and retrieval of information using logical connectives and         information from MEKB. This new information can also
quantifiers. The benefit of using Markov Logics is that            become par of the medical inference engine, if required.
the queries which even do match up to 80% will also be
answered as this is not the case in typical knowledgebase          d. Medical Explanation Module
that used production rules. This approach will increase
the response rate of the knowledge base and makes it               This is another very important module of the designed
more effective and efficient.                                      system. This module provides the facility of explaining
                                                                   and reasoning of the system to the user. User can make
                                                                   different queries regarding the system domain and
          Input Text (Patient’s Symptom Report)                    system.

                                                                   3.2. Algorithm for Query Processing
                                                                   For diagnosis and treatment of the patient, two techniques
                              Morphological           POS
     Tokenization                                                  are used in the proposed system. First and major
                                Analysis             Tagging
                                                                   technique to develop virtual telemedicine is “Rule Based
                                                                   Approach” in which is the most efficient way to represent
                                                                   human activity in the form of rules. Used algorithm has
      Pragmatic                Semantic               Lexical      two major parts. First part has been designed to read the
       Analysis                Analysis               Analysis
                                                                   patient’s symptoms of diseases and analyze according to
                                                                   the given knowledge base and diagnose the accurate
                                                                   disease. Second part of the designed algorithm prescribes
                                                                   the suitable medicine to the patient. Following steps are
      Medical Inference Engine                        MEKB         followed by the algorithm to diagnose a particular
                                                                   disease:

                                                                   Step –I Health care person collects the patient’s disease
     Diet Details       Medication              Exercise Details   information along with the symptoms of disease and
                                                                   records in the simple English form.
                                                                   Step –II The patient’s case information in the textual
                     Dose
                                                                   form is given to the designed virtual telemedicine system.
                                       Side
                    Details           Effects                      Step – III Natural language processing is performed to
                                                                   read the given text and extract the related information.
        Figure 3.3 - Working of the designed system                Used NLP steps to analyze text are [15]:
                                                                       o Tokenization (Separating tokens): The input
MEKB handles two types of rules Factual knowledge and
                                                                         sentence is tokenized into complete words
Heuristic Knowledge [19].
                                                                       o Morphology (to identify and analyze morphemes).
•   Factual Knowledge is the descriptive information. It                 The input of the previous step is further processed
    is basic knowledge related to the domain i.e.                        to identify the complete words and then identify
    Bacterial causes flue” Or “Dust allergy causes                       their parts of speech (POS) category.
    cough”.
o Lexical analysis (to identify grammatical types of     based wireless internet work or WiFi system supporting
      the tokens): The POS tagged words are further          speed of 1.0 Gbps or above is required. 3G cellular
      processed to identify their particular role in the     technology is also getting very popular these days [16] in
      sentence and grammatical rules also assist this        the field of telehealth. This technology can help out in
      type of analysis.                                      fast video sharing, video male and video conferencing.
                                                             On the other side, a telemedicine center at the remote
    o Semantic analysis (To understand the meanings of
                                                             area needs basic eequipments [12] i.e.
      the sentences): Different constituents of a sentence
      are analyzed here to extract the both implicit and     •   Virtual telemedicine software
      explicit meanings of the input text.                   •   Camera (s), lights, projector
    o Pragmatic analysis (to find out meanings in a          •   Digital X-Ray System
      particular context): This is an additional step that   •   UPS system
      is used if the meanings of the input text are not      •   Computer hardware, system and application software
      clear it is analyzed into its particular context to        and accessories
      make the things more clear and concise.
Step –IV Pattern matching of the extracted information is    5. EXPERIMENTS AND RESULTS
performed through the medical inference engine with the
information in medical expert knowledge base to find out     A number of experiments were performed to test the
patient’s disease.                                           designed health care system. A medical assistant was
Step –V If a match is found then treatment of the disease    involved to use the system. A multiple step procedure is
is recommended with the appropriate medication and           involved to use the designed medical health care system.
health instructions. If match is not found then the query    The steps are following:
is forwarded to the medical expert.                          1. Patient Registration
In a case, if the virtual medical expert does not find any   2. Patient Record File Generation
particular solution of the patient’s query from its          3. Processing User Details
knowledge base, rather related to the disease diagnosing     4. Generating Patient Report
or prescribing medicine, an automatic email is sent to the
medical expert. Medical expert examines the query with       Brief description of all these phases with the help of a
available facts and makes some decisions and replies the     case study has been provided in the later part of the
local medical assistant. The designed system also updates    section.
the Medical Expert Knowledge Base (MEKB) so that if
                                                             5.1. Patient Registration
the same query comes in future, it may be resolved
locally.                                                     A patient is needed to register with his personal details
                                                             i.e. name, age, sex, address, family history, previous
                                                             cases, etc for using the proposed virtual telemedicine
4. IMPLEMENTATION DETAILS                                    system. Figure 4.1 shows the form that is used to register
Rural areas of Pakistan [1] are relatively backward in       the patient first.
terms of technology. A number of challenges are to face
in setting up a system for virtual telemedicine. Some of
the major challenges are following:

•   Budget and financial constraints are more significant
    [14]. First of all expensive medical equipments are
    required at the telemedicine centers. High bandwidth
    for communication is also an expensive solution.
•   At the site, adequate human resources are required
    [15] i.e. technicians to implement the proposed
    virtual Telemedicine system, a medical assistant
    having medical training to perform basic tests of the
    patients and some health workers having basic
    literacy of computer and capable of using computers.

A complete infrastructure is required to actually set up
                                                                      Figure 5.1 – Form to register a patient
the proposed virtual telemedicine framework. A satellite
5.2. Patient Record File Generation
                                                                                           S
After registration, the medical expert performs basic tests
of a patient to get the reading of temperature, blood
pressure, blood group, sugar level and ESG (if required).                   NP                         VP
Then he records the common symptoms of the patient in
the system. Besides these tests, the data i.e. color of              Det.                       Verb                   NP
                                                                                 Noun
tongue, color of eyes, heart beat, face color, etc is also
captured and is updated in the system. The data form is
shown in the figure 4.2                                              The         patient        H.V.           Adej             Noun


                                                                                                 has           high             fever

                                                                      Figure 1.0- Parse tree generated for the example

                                                                 There are two rationales for performing the syntactic
                                                                 analysis; to validate the phrases and sentence according
                                                                 to grammatical rules defined by the English language and
                                                                 finding out the semantical constituents of natural
                                                                 language. Moreover, the semantical analysis helps in
                                                                 identifying the main parts of a sentence i.e. object,
                                                                 subject, actions, attributes, etc.

                                                                      [The] [patient]          [has]        [high]    [fever]     [.]

                                                                            Subject            Verb             Object
                                                                 In this step, associations are identified by doing semantic
         Figure 5.2 – Form to update patient’s status            analysis. It is determined in this specified that which
                                                                 actions have been performed by which object and a set of
Medical assistant can also use digital stethoscope and           attributes belong to which object e.g. in the above
electrocardiograph file with ECG recorder or images with         example it is extracted that a person is having a high
the examination camera [13]. A text file containing the          fever.
patient’s case details is prepared.
                                                                 5.4. Generating Patient Report
5.3. Processing User Details
                                                                 Afterwards, the extracted information of the last phase is
The input text file containing the patient’s history and         matched with the knowledge in MEKB. Inference engine
symptoms is given to the designed system foe processing.         extracts the desired information and processes the
In first step, input is read and tokenized e.g. the output of    patient’s symptoms to infer the disease. If disease is
a sentence “The patient has high fever.” is                      found then the respective medication of the disease and
                                                                 additional information i.e. diet and exercise details are
            [The] [patient] [has] [high] [fever] [.]             also provided. The designed system will not only provide
                                                                 the treatment strategy of the disease but also recommends
After tokenizing the text, morphological analysis is
                                                                 tests if necessary for the confirmation of the disease. If
performed of given text to define the structuring and
                                                                 the tests were recommended by the system to the patient,
transformation of the words. POS Tagging is also
                                                                 patient/administrator will have to provide the results of
performed to identify different parts of speech e.g.
                                                                 the tests to the system so that system may recommend the
 [The]       [patient]     [has]      [high]       [fever] [.]   right treatment of the disease. If the system is not able to
                                                                 answer the patient then an automatic e-mail will be
                                                                 forwarded to the medical expert. The medical expert will
Determiner     Noun        Verb      adjective         Noun      carefully examine the case by consulting all the test
                                                                 reports and data sent by the local medical assistant and
After POS tagging, the text is lexically and syntactically
                                                                 diagnosis the disease and also prescribes the appropriate
analyzed and a parse tree is generated for semantic
                                                                 medication. When the medical assistant receives the
analysis. Figure 5.3 shows the generated parse tree of the
                                                                 response, the medical expert’s opinion is also updated in
above example.
                                                                 the knowledge-base of the system.
A prescription will be generated after the patient’s data is   usability of the Virtual telemedicine will be more useful
submitted. If the knowledge base cannot reply then the         and beneficial. The experiments were performed on a
patient’s data will be emailed to expert. The correctness      simulator and it is acceptable that these results may vary
of the decision made by the software and the medical           when the system will be run real time. In future
expert is based on the accuracy of the data captured by        enhancements the algorithms is needed to be improved to
the medical assistant. The quality and accurateness of the     increase the accuracy level of the system. Medical
images and video of the patient is also quite important.       explanation module is also needed to enhance its
                                                               usability.
To validate the precision and affectivity of the designed
system symptom reports of three groups of ten patients         References
were defined. For each group three reports i.e. easy,
average an difficult were generated for each group. The        [1] M. Z. Khalid, A. Akbar, A. Kumar , A. Tariq, M. Farooq,
symptom reports were carefully prepared and processed            [2008] “Using Telemedicine as an Enabler for Antenatal Care
for each patient using the designed health care system.          in Pakistan”, Proc. 2nd International Conference: E-Medical
For correct and wrong diagnosis of a symptom report              System, Oct 2008, Tunisia, pp 1-8
various points were given. Table 5.1 shows the details of
                                                               [2] Tayab Din Memon, BS Chowdhry, AK Baloch, “Design and
the results.                                                      Implementation of a Telecardiologic System”, MUET,
                                                                  Research Journal, Volume 23, No. 4, Oct 2004.
            Group 1 Group 2 Group 3 Total             %
                                                               [3] William R., David H., Susan M., Tracy L., Kathryn Pyle,
Easy        10/10     9/10       9/10        2.8      93.33      Mark Helfand, [2006] “Telemedicine for the Medicare
                                                                 Population: Update”, AHRQ publication No. 06-E007
Average     9/10      8/10       9/10        2.6      86.66
Difficult   7/10      8/10       8/10        2.3      76.66    [4] Thomas S. Nesbitt, [2007] “Meeting the Health Care Needs
                                                                 of California’s Children: The Role of Telemedicine”, Digital
                                Average Accuracy: 85.5%          Opportunity for Youth Issue Brief, Number 3: September
                                                                 2007
Table 5.1 Virtual Telemedicine Based Healthcare System
                                                               [5] Albert M. Lai, Jason Nieh, Justin B., [2007] “REPETE2: A
Following are some benefits over using the proposed              Next Generation Home Telemedicine Architecture”, AMIA
framework virtual telemedicine.                                  2007 Symposium Proceedings, pp 1020-1022

•   Improved and immediate access the specialty care           [6] Lai AM and Nieh J., [2006] “On the Performance of Wide-
•   Upgraded emergency medical services                          Area Thin-Client Computing”, ACM Transaction on
                                                                 Computer Systems. May 2006, p. 215-209
•   Reduction in un-necessary duplication of services
•   Less dependency on the medical expert
                                                               [7]. Lai AM and Nieh J., [2005] “Web Content Delivery Using
•   Easier diagnostic consultation                                Thin-Client Computing”, In: Chanson ST, Xu TJ, editors.
•   Expanded disease cure education                               Web Content Delivery. Springer; 2005. pp. 325-346
•   More patient health queries
•   Remote medical consultation                                [8] Dena Puskin, Barbara and Stuart, [2006] “Telemedicine,
•   Reduction in health care cost                                Telehealth, and Health Information Technology”, An ATA
                                                                 Issue Paper, The American Telemedicine Association, May
•   Automated patient record keeping
                                                                 2006

6. CONCLUSION & FUTURE WORK                                    [9] Dena S. Puskin, [1995] “Opportunities and challenges to
                                                                 telemedicine in rural America”, Journal of Medical Systems,
Virtual Telemedicine is the new concept which actually           Volume 19, Number 1 / February, 1995, pp 59-67
works faster than that of the traditional telemedicine
systems. An expert system has been deployed in place of        [10] E. J. Gomez, F. Del Pozo, M. Hernando, “Telemedicine for
a medical expert that has ability to immediate respond.          diabetes care: The DIABTel approach towards diabetes
                                                                 telecare”, Informatics for Health and Social Care, Volume
This immediate response can help to treat patients in time       21, Issue 4 October 1996 , pages 283 – 295
and more effectively. 90% queries can be entertained
locally. The accuracy achieved with the designed system        [11] D A Perednia and A. Allen, “Telemedicine Technology and
is 85.5%. The Virtual expert system becomes more robust          Clinical Applications”, The Journal of the American Medical
and intelligent with the passage of time as the                  Association, 273(6), 8 Feb 1995, pp. 483–88.
knowledge-base grows and the level accuracy will also
improve. For the under developed and developing
countries like Pakistan, Bangladesh, Sri Lanka etc the
[12] C A Sable, et al., “Impact of Telemedicine on the Practice   Report”, Telemedicine Journal and e-Health, Vol 8, No 2,
  of Pediatric Cardiology in Community Hospitals”, Pediatrics,    2002.
  January 2002, Vol. 109, No. 1 pp. e3.

[13] Rashid E, Ishtiaq O, Gilani S, et al. “Comparison of store
  and forward method of teledermatology with face-to-face
  consultation” J Ayub Med Coll Abbottabad 2003 Apr-Jun;15
  (2):34-6.

[14] J. Jiehui, Z. Jing, [2007] ‘Remote patient monitoring
  system for China.’ IEEE Potentials, Vol. 26, Issue 3, pp 26-
  29, IEEE, May/June 2007.

[15] Bajwa I., Choudhary I. [2006] “A Rule Based Paradigm for
  Speech Language Context Understanding”, J Donghua
  University (English Edition) Jun 2006, 23(6), pp.39-42

[16] Hersh W, Helfand M, Wallace J, [2002] “A systematic
  review of the efficacy of telemedicine for making diagnostic
  and management decisions”, J Telemed Telecare 8(4):197-
  209.

[17] Andrew J Diver, Harry Lewis, Derek J Gordon, (2009),
  “Telemedicine and Trauma Referrals – a Plastic Surgery
  Pilot Project”, Ulster Med J 2009 78(2), pp. 113-114

[18] Demartines N, Otto U, Mutter D, Labler L, van Weymarn
  A Vix M, et al. “An evaluation of telemedicine in surgery:
  tele-diagnosis compared with direct diagnosis” Arch Surg
  2000 135(7), pp. 849-53.

[19] Shahbaz F., Maqbool F., Razzaq S., Irfan K., Zia T.,
  [2008] “The Role of Medical Expert Systems in Pakistan”,
  World Academy of Science, Engineering and Technology
  2008 Vol. 37 pp. 296-298

[20] Whitten PS, Mair FS, Haycox A, May CR, Williams TL,
  Hellmich S. et al. “Systematic review of cost effectiveness
  studies of telemedicine interventions” BMJ 2002; 324(7351)
  pp. 1434-1437.

[21] Yellowlees P. “Successful development of telemedicine
  systems – seven core principles” J Telemed Telecare 1997;
  3(4), pp. 215-222.

[22] M P Cutchin, “Virtual Medical Geographies:
  Conceptualizing Telemedicine and Regionalization”,
  Progress in Human Geography, 26, 1, 2002, pp. 19–39

[23] G Zahlman and S Laxminarayan, “Special Issue on
  Telemedical Systems, Guest Editorial”, IEEE Transactions
  on Information Technology in Biomedicine, Vol 3, Number
  2, June 1999.

[24]L Kun (1998), “Biomedical Information Technology:
  Opportunities for the Future”, Proceedings ITAB ‘98,
  Washington, DC, (Ed S Laxminarayan and E M Tzanakou),
  IEEE Press.

[25] A Lacroix, et al., “International Concerted Action on
  Collaboration in Telemedicine: Recommendations of the G-8
  Global Healthcare Applications Subproject-4, Special

More Related Content

What's hot

Rural Telemedicine Network India
Rural Telemedicine Network IndiaRural Telemedicine Network India
Rural Telemedicine Network Indiadr.md
 
Worldwide telemedicine initiatives status and
Worldwide telemedicine initiatives status andWorldwide telemedicine initiatives status and
Worldwide telemedicine initiatives status andRubashkyn
 
Telemedicine ppt
Telemedicine pptTelemedicine ppt
Telemedicine pptkhandhar
 
Using digital videos displayed on personal digital assistants (pd_as) to enha...
Using digital videos displayed on personal digital assistants (pd_as) to enha...Using digital videos displayed on personal digital assistants (pd_as) to enha...
Using digital videos displayed on personal digital assistants (pd_as) to enha...InSTEDD
 
E Health Research Project
E Health Research ProjectE Health Research Project
E Health Research Projectdlozeva
 
Telemedicine presentation feb. 2014
Telemedicine presentation feb. 2014Telemedicine presentation feb. 2014
Telemedicine presentation feb. 2014Howard Reis
 
About Telemedicine in digital communication
About Telemedicine in digital communication About Telemedicine in digital communication
About Telemedicine in digital communication Ami Goswami
 
All about telemedicine
All about telemedicineAll about telemedicine
All about telemedicineCare Clix
 
Insights2020 Telemedicine Comes Forward
Insights2020 Telemedicine Comes ForwardInsights2020 Telemedicine Comes Forward
Insights2020 Telemedicine Comes ForwardBen Quirk
 
Telemedicine PPT FOR QUICK PRESENTATION
Telemedicine PPT FOR QUICK PRESENTATIONTelemedicine PPT FOR QUICK PRESENTATION
Telemedicine PPT FOR QUICK PRESENTATIONHARSH VARDHAN
 
Telemedicine Guidelines
Telemedicine GuidelinesTelemedicine Guidelines
Telemedicine GuidelinesTiE Bangalore
 
Telemedicine Practice Guidelines for ASU
Telemedicine Practice Guidelines for ASU   Telemedicine Practice Guidelines for ASU
Telemedicine Practice Guidelines for ASU Sanjay Sharma
 
ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...
ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...
ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...Deploy360 Programme (Internet Society)
 
OTTET Telemedicine
OTTET TelemedicineOTTET Telemedicine
OTTET Telemedicineottet
 

What's hot (20)

Telemedicine
TelemedicineTelemedicine
Telemedicine
 
Rural Telemedicine Network India
Rural Telemedicine Network IndiaRural Telemedicine Network India
Rural Telemedicine Network India
 
Telehealth Report - India
Telehealth Report - India  Telehealth Report - India
Telehealth Report - India
 
Worldwide telemedicine initiatives status and
Worldwide telemedicine initiatives status andWorldwide telemedicine initiatives status and
Worldwide telemedicine initiatives status and
 
Telemedicine ppt
Telemedicine pptTelemedicine ppt
Telemedicine ppt
 
Using digital videos displayed on personal digital assistants (pd_as) to enha...
Using digital videos displayed on personal digital assistants (pd_as) to enha...Using digital videos displayed on personal digital assistants (pd_as) to enha...
Using digital videos displayed on personal digital assistants (pd_as) to enha...
 
E Health Research Project
E Health Research ProjectE Health Research Project
E Health Research Project
 
Telemedicine presentation feb. 2014
Telemedicine presentation feb. 2014Telemedicine presentation feb. 2014
Telemedicine presentation feb. 2014
 
Telehealth
TelehealthTelehealth
Telehealth
 
About Telemedicine in digital communication
About Telemedicine in digital communication About Telemedicine in digital communication
About Telemedicine in digital communication
 
All about telemedicine
All about telemedicineAll about telemedicine
All about telemedicine
 
Insights2020 Telemedicine Comes Forward
Insights2020 Telemedicine Comes ForwardInsights2020 Telemedicine Comes Forward
Insights2020 Telemedicine Comes Forward
 
Tele Doctor - Integrated Telemedicine System
Tele Doctor - Integrated Telemedicine SystemTele Doctor - Integrated Telemedicine System
Tele Doctor - Integrated Telemedicine System
 
Telemedicine PPT FOR QUICK PRESENTATION
Telemedicine PPT FOR QUICK PRESENTATIONTelemedicine PPT FOR QUICK PRESENTATION
Telemedicine PPT FOR QUICK PRESENTATION
 
Telemedicine Guidelines
Telemedicine GuidelinesTelemedicine Guidelines
Telemedicine Guidelines
 
Abc of telemedicine
Abc of telemedicineAbc of telemedicine
Abc of telemedicine
 
Telemedicine Practice Guidelines for ASU
Telemedicine Practice Guidelines for ASU   Telemedicine Practice Guidelines for ASU
Telemedicine Practice Guidelines for ASU
 
ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...
ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...
ION Bangladesh Keynote - Potential of Indigenously Developed Telemedicine usi...
 
Telemedicine
TelemedicineTelemedicine
Telemedicine
 
OTTET Telemedicine
OTTET TelemedicineOTTET Telemedicine
OTTET Telemedicine
 

Viewers also liked (14)

Capacity
CapacityCapacity
Capacity
 
UML Generator (NCC18)
UML Generator (NCC18)UML Generator (NCC18)
UML Generator (NCC18)
 
Pres
PresPres
Pres
 
Domain Specific Terminology Extraction (ICICT 2006)
Domain Specific Terminology Extraction (ICICT 2006)Domain Specific Terminology Extraction (ICICT 2006)
Domain Specific Terminology Extraction (ICICT 2006)
 
Image Classification (icast 2006)
Image Classification  (icast 2006)Image Classification  (icast 2006)
Image Classification (icast 2006)
 
Automated Java Code Generation (ICDIM 2006)
Automated Java Code Generation (ICDIM 2006)Automated Java Code Generation (ICDIM 2006)
Automated Java Code Generation (ICDIM 2006)
 
capacity
capacitycapacity
capacity
 
NL based Object Oriented modeling - EJSR 35(1)
NL based Object Oriented modeling - EJSR 35(1)NL based Object Oriented modeling - EJSR 35(1)
NL based Object Oriented modeling - EJSR 35(1)
 
Dinamicas grupo uch
Dinamicas grupo uchDinamicas grupo uch
Dinamicas grupo uch
 
Ley51 rev
Ley51 revLey51 rev
Ley51 rev
 
El teclado
El tecladoEl teclado
El teclado
 
Gaceta municipal 1
Gaceta municipal 1Gaceta municipal 1
Gaceta municipal 1
 
Russian History Gulf Coast Community College Fall Part 2, Class 4
Russian History Gulf Coast Community College Fall Part 2, Class 4Russian History Gulf Coast Community College Fall Part 2, Class 4
Russian History Gulf Coast Community College Fall Part 2, Class 4
 
Editor2 spanish
Editor2 spanishEditor2 spanish
Editor2 spanish
 

Similar to Virtual Telemedicine (IJITWE 5(1))

Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...
Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...
Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...Eswar Publications
 
Itec 610 group presentation final
Itec 610   group presentation finalItec 610   group presentation final
Itec 610 group presentation finalnoelmacias
 
Web based database management to support telemedicine system
Web based database management to support telemedicine systemWeb based database management to support telemedicine system
Web based database management to support telemedicine systemijait
 
Telemedicine in COVID19 pandemic
Telemedicine in COVID19 pandemicTelemedicine in COVID19 pandemic
Telemedicine in COVID19 pandemicRosalindSilverman
 
Informatics And Telehealth In Rural Medicines TedEx Video Analysis.pdf
Informatics And Telehealth In Rural Medicines TedEx Video Analysis.pdfInformatics And Telehealth In Rural Medicines TedEx Video Analysis.pdf
Informatics And Telehealth In Rural Medicines TedEx Video Analysis.pdfbkbk37
 
Pvcs For Telemedicine
Pvcs For TelemedicinePvcs For Telemedicine
Pvcs For TelemedicineRamakrishna
 
Telemedicine, telenursing, nursing informatics,e nursing
Telemedicine, telenursing, nursing informatics,e nursingTelemedicine, telenursing, nursing informatics,e nursing
Telemedicine, telenursing, nursing informatics,e nursingArvind joshi
 
Chapter 8 Telehealth and Applications for Delivering Care at a Dis.docx
Chapter 8 Telehealth and Applications for Delivering Care at a Dis.docxChapter 8 Telehealth and Applications for Delivering Care at a Dis.docx
Chapter 8 Telehealth and Applications for Delivering Care at a Dis.docxchristinemaritza
 
Telecommunication systems applied to telemedicine
Telecommunication systems applied to telemedicineTelecommunication systems applied to telemedicine
Telecommunication systems applied to telemedicineShazia Iqbal
 
WAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docx
WAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docxWAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docx
WAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docxcelenarouzie
 
(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docx
(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docx(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docx
(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docxAASTHA76
 
TELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIES
TELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIESTELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIES
TELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIESRubashkyn
 

Similar to Virtual Telemedicine (IJITWE 5(1)) (20)

F07013840
F07013840F07013840
F07013840
 
Telemedicine
TelemedicineTelemedicine
Telemedicine
 
Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...
Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...
Implementation of Remote Health Monitoring in Medical Rural Clinics for Web T...
 
Tele-medicine and Tele-nursing
Tele-medicine and Tele-nursing Tele-medicine and Tele-nursing
Tele-medicine and Tele-nursing
 
Itec 610 group presentation final
Itec 610   group presentation finalItec 610   group presentation final
Itec 610 group presentation final
 
Web based database management to support telemedicine system
Web based database management to support telemedicine systemWeb based database management to support telemedicine system
Web based database management to support telemedicine system
 
Telehealth Lessons Learned
Telehealth Lessons LearnedTelehealth Lessons Learned
Telehealth Lessons Learned
 
NCM 118-Group-1.pdf
NCM 118-Group-1.pdfNCM 118-Group-1.pdf
NCM 118-Group-1.pdf
 
Telemedicine in COVID19 pandemic
Telemedicine in COVID19 pandemicTelemedicine in COVID19 pandemic
Telemedicine in COVID19 pandemic
 
Informatics And Telehealth In Rural Medicines TedEx Video Analysis.pdf
Informatics And Telehealth In Rural Medicines TedEx Video Analysis.pdfInformatics And Telehealth In Rural Medicines TedEx Video Analysis.pdf
Informatics And Telehealth In Rural Medicines TedEx Video Analysis.pdf
 
E nursing corrected
E nursing correctedE nursing corrected
E nursing corrected
 
Telemedicine
TelemedicineTelemedicine
Telemedicine
 
Telemedicine
TelemedicineTelemedicine
Telemedicine
 
Pvcs For Telemedicine
Pvcs For TelemedicinePvcs For Telemedicine
Pvcs For Telemedicine
 
Telemedicine, telenursing, nursing informatics,e nursing
Telemedicine, telenursing, nursing informatics,e nursingTelemedicine, telenursing, nursing informatics,e nursing
Telemedicine, telenursing, nursing informatics,e nursing
 
Chapter 8 Telehealth and Applications for Delivering Care at a Dis.docx
Chapter 8 Telehealth and Applications for Delivering Care at a Dis.docxChapter 8 Telehealth and Applications for Delivering Care at a Dis.docx
Chapter 8 Telehealth and Applications for Delivering Care at a Dis.docx
 
Telecommunication systems applied to telemedicine
Telecommunication systems applied to telemedicineTelecommunication systems applied to telemedicine
Telecommunication systems applied to telemedicine
 
WAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docx
WAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docxWAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docx
WAL_HUMN1020_03_A_EN-CC.mp4Chapter 8 Telehealth and Applicat.docx
 
(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docx
(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docx(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docx
(Glossary of Telemedicine and eHealth)· Teleconsultation Cons.docx
 
TELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIES
TELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIESTELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIES
TELEMEDICINE AND HEALTH INFORMATION TECHNOLOGIES
 

More from IT Industry

The News Today 24 (https://thenewstoday24.com/)
The News Today 24 (https://thenewstoday24.com/)The News Today 24 (https://thenewstoday24.com/)
The News Today 24 (https://thenewstoday24.com/)IT Industry
 
Meaning Extraction - IJCTE 2(1)
Meaning Extraction - IJCTE 2(1)Meaning Extraction - IJCTE 2(1)
Meaning Extraction - IJCTE 2(1)IT Industry
 
NL Interface for Database - EJSR 20(4)
NL Interface for Database - EJSR 20(4)NL Interface for Database - EJSR 20(4)
NL Interface for Database - EJSR 20(4)IT Industry
 
Requirement Analysis - ijcee 2(3)
Requirement Analysis - ijcee 2(3)Requirement Analysis - ijcee 2(3)
Requirement Analysis - ijcee 2(3)IT Industry
 
NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)IT Industry
 
BPM & SOA for Small Business Enterprises (ICIME 2009)
BPM & SOA for Small Business Enterprises (ICIME 2009)BPM & SOA for Small Business Enterprises (ICIME 2009)
BPM & SOA for Small Business Enterprises (ICIME 2009)IT Industry
 
Web Layout Mining - JECS 29(2)
Web Layout Mining - JECS 29(2)Web Layout Mining - JECS 29(2)
Web Layout Mining - JECS 29(2)IT Industry
 
UCD Generator (ICIET 2007)
UCD Generator (ICIET 2007)UCD Generator (ICIET 2007)
UCD Generator (ICIET 2007)IT Industry
 
Web User Forms (ICOMMS 2006)
Web User Forms (ICOMMS 2006)Web User Forms (ICOMMS 2006)
Web User Forms (ICOMMS 2006)IT Industry
 
Reuse Software Components (IMS 2006)
Reuse Software Components (IMS 2006)Reuse Software Components (IMS 2006)
Reuse Software Components (IMS 2006)IT Industry
 
GIS for Quetta (ICAST 2006)
GIS for Quetta (ICAST 2006)GIS for Quetta (ICAST 2006)
GIS for Quetta (ICAST 2006)IT Industry
 
NL Context Understanding 23(6)
NL Context Understanding 23(6)NL Context Understanding 23(6)
NL Context Understanding 23(6)IT Industry
 
Web Layout Generation (IC-SCCE 2006)
Web Layout Generation (IC-SCCE 2006)Web Layout Generation (IC-SCCE 2006)
Web Layout Generation (IC-SCCE 2006)IT Industry
 
PCA Clouds (ICET 2005)
PCA Clouds (ICET 2005)PCA Clouds (ICET 2005)
PCA Clouds (ICET 2005)IT Industry
 
Feature Based Image Classification by using Principal Component Analysis
Feature Based Image Classification by using Principal Component AnalysisFeature Based Image Classification by using Principal Component Analysis
Feature Based Image Classification by using Principal Component AnalysisIT Industry
 

More from IT Industry (15)

The News Today 24 (https://thenewstoday24.com/)
The News Today 24 (https://thenewstoday24.com/)The News Today 24 (https://thenewstoday24.com/)
The News Today 24 (https://thenewstoday24.com/)
 
Meaning Extraction - IJCTE 2(1)
Meaning Extraction - IJCTE 2(1)Meaning Extraction - IJCTE 2(1)
Meaning Extraction - IJCTE 2(1)
 
NL Interface for Database - EJSR 20(4)
NL Interface for Database - EJSR 20(4)NL Interface for Database - EJSR 20(4)
NL Interface for Database - EJSR 20(4)
 
Requirement Analysis - ijcee 2(3)
Requirement Analysis - ijcee 2(3)Requirement Analysis - ijcee 2(3)
Requirement Analysis - ijcee 2(3)
 
NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)NL to OCL Transformation (EDOC 2010)
NL to OCL Transformation (EDOC 2010)
 
BPM & SOA for Small Business Enterprises (ICIME 2009)
BPM & SOA for Small Business Enterprises (ICIME 2009)BPM & SOA for Small Business Enterprises (ICIME 2009)
BPM & SOA for Small Business Enterprises (ICIME 2009)
 
Web Layout Mining - JECS 29(2)
Web Layout Mining - JECS 29(2)Web Layout Mining - JECS 29(2)
Web Layout Mining - JECS 29(2)
 
UCD Generator (ICIET 2007)
UCD Generator (ICIET 2007)UCD Generator (ICIET 2007)
UCD Generator (ICIET 2007)
 
Web User Forms (ICOMMS 2006)
Web User Forms (ICOMMS 2006)Web User Forms (ICOMMS 2006)
Web User Forms (ICOMMS 2006)
 
Reuse Software Components (IMS 2006)
Reuse Software Components (IMS 2006)Reuse Software Components (IMS 2006)
Reuse Software Components (IMS 2006)
 
GIS for Quetta (ICAST 2006)
GIS for Quetta (ICAST 2006)GIS for Quetta (ICAST 2006)
GIS for Quetta (ICAST 2006)
 
NL Context Understanding 23(6)
NL Context Understanding 23(6)NL Context Understanding 23(6)
NL Context Understanding 23(6)
 
Web Layout Generation (IC-SCCE 2006)
Web Layout Generation (IC-SCCE 2006)Web Layout Generation (IC-SCCE 2006)
Web Layout Generation (IC-SCCE 2006)
 
PCA Clouds (ICET 2005)
PCA Clouds (ICET 2005)PCA Clouds (ICET 2005)
PCA Clouds (ICET 2005)
 
Feature Based Image Classification by using Principal Component Analysis
Feature Based Image Classification by using Principal Component AnalysisFeature Based Image Classification by using Principal Component Analysis
Feature Based Image Classification by using Principal Component Analysis
 

Recently uploaded

In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxAditiChauhan701637
 
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptxPISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptxEduSkills OECD
 
Presentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a ParagraphPresentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a ParagraphNetziValdelomar1
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfYu Kanazawa / Osaka University
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxDr. Asif Anas
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationMJDuyan
 
The basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxThe basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxheathfieldcps1
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?TechSoup
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxDr. Santhosh Kumar. N
 
Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.EnglishCEIPdeSigeiro
 
Patterns of Written Texts Across Disciplines.pptx
Patterns of Written Texts Across Disciplines.pptxPatterns of Written Texts Across Disciplines.pptx
Patterns of Written Texts Across Disciplines.pptxMYDA ANGELICA SUAN
 
Human-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming ClassesHuman-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming ClassesMohammad Hassany
 
Education and training program in the hospital APR.pptx
Education and training program in the hospital APR.pptxEducation and training program in the hospital APR.pptx
Education and training program in the hospital APR.pptxraviapr7
 
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptxClinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptxraviapr7
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfMohonDas
 
UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE
 
Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.raviapr7
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17Celine George
 
Practical Research 1: Lesson 8 Writing the Thesis Statement.pptx
Practical Research 1: Lesson 8 Writing the Thesis Statement.pptxPractical Research 1: Lesson 8 Writing the Thesis Statement.pptx
Practical Research 1: Lesson 8 Writing the Thesis Statement.pptxKatherine Villaluna
 
5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...CaraSkikne1
 

Recently uploaded (20)

In - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptxIn - Vivo and In - Vitro Correlation.pptx
In - Vivo and In - Vitro Correlation.pptx
 
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptxPISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
PISA-VET launch_El Iza Mohamedou_19 March 2024.pptx
 
Presentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a ParagraphPresentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a Paragraph
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptx
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive Education
 
The basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxThe basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptx
 
What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?What is the Future of QuickBooks DeskTop?
What is the Future of QuickBooks DeskTop?
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptx
 
Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.
 
Patterns of Written Texts Across Disciplines.pptx
Patterns of Written Texts Across Disciplines.pptxPatterns of Written Texts Across Disciplines.pptx
Patterns of Written Texts Across Disciplines.pptx
 
Human-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming ClassesHuman-AI Co-Creation of Worked Examples for Programming Classes
Human-AI Co-Creation of Worked Examples for Programming Classes
 
Education and training program in the hospital APR.pptx
Education and training program in the hospital APR.pptxEducation and training program in the hospital APR.pptx
Education and training program in the hospital APR.pptx
 
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptxClinical Pharmacy  Introduction to Clinical Pharmacy, Concept of clinical pptx
Clinical Pharmacy Introduction to Clinical Pharmacy, Concept of clinical pptx
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdf
 
UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024
 
Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17
 
Practical Research 1: Lesson 8 Writing the Thesis Statement.pptx
Practical Research 1: Lesson 8 Writing the Thesis Statement.pptxPractical Research 1: Lesson 8 Writing the Thesis Statement.pptx
Practical Research 1: Lesson 8 Writing the Thesis Statement.pptx
 
5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...
 

Virtual Telemedicine (IJITWE 5(1))

  • 1. Virtual Telemedicine using Natural Language Processing Imran Sarwar Bajwa Department of Computer Science & IT The Islamia University of Bahawalpur imran.sarwar@iub.edu.pk Abstract way of getting medical treatment at home. Health care facilities can be improved for a specific community: Conventional telemedicine can be inept due to the existing time children, old people, plague disease, etc. Telemedicine constraints in response of the medical specialist. One major can become moiré effective in emergency cases and areas reason is that telemedicine based medical facilities are subject of natural disasters. Still, this is cost effective and to the availability of the medical expert and telecommunication facilities, when they are required. On the other hand, efficient way of providing high level and skilled medical communication using telecommunication is only possible on facilities to the people living in remote areas [2], who can fixed and appointed time. Typically, the field of telemedicine easily access the physicians and medical specialist. exists in both medical and telecommunication areas to provide medical facilities over long distance especially in remote areas. 1.1 Conventional Telemedicine In this paper, a solution ‘virtual telemedicine’ is presented to cope up with the problem of long time constraint that is faced in Telemedicine typically works in two ways [3]: store and conventional telemedicine. Virtual Telemedicine is the use of forward method and real time method. Store and forward telemedicine with the methods of artificial intelligence to over method gathers patient’s medical information locally and come the problems of telemedicine. Virtual medicine uses a then patient query is emailed to a physician. Afterwards, virtual physician that can treat patients anywhere, any time in physician prescribes a treatment and then emails the remote areas as well. Virtual telemedicine can be accessed response of the medical query in 24 to 48 hours. On the online as well. other hand, in real time telemedicine, video conferencing Keywords – Telemedicine, Telecommunication for health, and live data transmission methods are involved for Information retrieval, Text Processing, Expert system communication between patient and medical expert. UCD Health system [4] is one of the examples of video conference based health systems. 1. INTRODUCTION Telecommunication is the most used technology all over the world in current age and still establishing a long way. This technology has made things to do in an easy and fast manner. Now enhancements in technology have made our thoughts to drag fields of life into advance technology. From last few years, alphabet ‘e’ is being used with almost everything i.e. e-mail, e-learning, e-commerce, e- banking and e-services. The proposal of ‘e-health’ is still new and asks for more development. Medical is the field that is emerging continuously to make health facilities Figure-1.1: UCD Health system – Patient side [4] more affective and facilitating. Telemedicine [1] is the need of current age to provide health facilities in the remote areas where medical experts, doctors and physicians are not available. Telemedicine uses telecommunication technology to provide medical treatment and services. Telemedicine connects patients with doctors where distance is a critical factor and exchanges the information of diagnosis, treatment and other health care activities. Telemedicine becomes more significant if the patient is far away from the medical experts and faces transportation challenges. On the other hand it is helpful Figure-1.2: UCD Health system – Physician side [4]
  • 2. In store and forward method is quite approving solution researchers who realized the importance and need of the but it requires lot of time to get diagnostic results in telemedicine based medical facilities. His study return. Time constraint can be up to 24 to 48 hours. In elaborated the use of store and forward method of real time telemedicine, there are so many constraints that medical information transformation. His work also make its effective usability difficult. While in countries emphasizes the need of efficient use of the resources to like Pakistan [1] where video conferencing is a pricey make the telemedicine based health care system more client, real mode is not appropriate solution. Secondly, effective and useful. Albert and Jason conducted two high bandwidth is required for data transmission. On the preliminary studies [5] in year 2007 to examine the other hand, the availability of the medical expert is also performance of remote display protocol (RDP) used in required, when the patients need. Virtual telemedicine is telemedicine systems. In first study, RDP was deployed the process to provide the telemedicine features online in a wide-area network [6] and in second one, the using a virtual physician in place of the real doctor. Other performance of RDP was analyzed over Wi-Fi [7]. They famous telemedicine types are home telemedicine and also presented a thin client based home telemedicine individual telemedicine [3]. architecture that was providing remote training for patients on broadband. 1.2. Virtual Telemedicine Dena S. [9] discussed uses and benefits of telemedicine As we have discussed in the previous section, that store typically for rural areas in America. She presented that and forward method is reasonably practicable but the considerable technical, organizational, and financial time constraint of store this method is not realistic. As obstacles have kept the rural communities deprived of some times due to the serious condition of the patient, benefits of the technology. This paper focuses on these he/she may not wait for up to 48 hours [11]. Some issues and suggests a feasible solution for establishing intelligent mechanism is required to improve the usability successful rural telemedicine programs. DIABTel [10] and affectivity of the conventional telemedicine process. Telemedicine Service is another telemedicine based An intelligent system is required that may provide system that provides daily care to diabetic patients. Major immediate response. In conventional telemedicine, an concern of the research was to provide telemonitoring of additional component is proposed in this research: virtual patient's blood glucose data and also support remote care physician. Virtual physician is a web-based application from doctors to diabetic patients. Tayab D. [2] proposed a that answers without delay the medical queries. To make cost effective and multipurpose model of the this facility more comprehensive, an additional telemedicine system. The proposed system had two major functionality of consultation is also involved. In this parts: a telemedicine unit for the patient side and another facility, if the knowledgebase of the virtual physician base unit for medical expert side. Major issue of cannot answer a medical query an automatic email is sent discussion was the use of high-speed network forms for to a medical expert and the response of the query is interconnectivity of the complete system. Dena Puskin, updated in the knowledgebase for future queries. Barbara and Stuart presented a framework [8] of a telehealth system that was able to identify and understand In this article, the section 2 presents the review of related the interaction between telemedicine services. work done be the various researchers in the field of Exploration of health information technology [8] (HIT) telemedicine and its applications in different areas of applications on local, regional and national levels was the healthcare. Section 3 highlights the architecture of the major emphasis of this research. designed medical expert system and the NLP based algorithm that process the textual information. Section 4 UC Davis used a telepharmacy program in UCD Health describes the implementation details and the section 5 System [4] that was based on a video conferencing. The presents a case study to elaborate the use of the designed author cites many challenges to telemedicine in the recent system and the results of the performed experiments with times i.e. system expertise, imprecise administration, the analysis are also provided in later half of the same contractual organization, etc. Tele-echocardiology [12] is section. another field of major research in telemedicine. This field of research deals with the real time diagnosis if heart diseases without the support of in-house pediatric 2. LITERATURE REVIEW cardiologists. The major emphasis of the research was to Field of telemedicine is being proved the technology of evaluate the impact of the telemedicine in providing the the electronic age. Although the telemedicine was first health care facilities to the cardiac patient in community time used in 1959 but major development work was hospitals where cardiac specialists are not available initiated in this field for the last 8 to 10 years. frequently. Telemedicine has been used for the e-health solution of In the recent times where wireless technologies are diseases: diabetes, cardiac, trauma, and general physician grasping their roots in other fields of life, at the same related diseases. P. Douglas [11] was one of the earlier
  • 3. time telemedicine is also getting benefits of it. An transmit patient’s information to the medical expert. Still advanced wireless sensor network (WSN) [] for health there are important issues like accurate information monitoring is introduced by G. Virone in DCS, exchange, security, transmission bandwidth, protocols, University of Virginia. The research presents a proposal data sets etc. ‘smart healthcare’ with the benefits of low cost and ad- hoc deployment of model sensors of for an improved quality of health care. A. Diver [17] has recently introduced his work to emphasize the significance of image analysis as an additional support for assure the modern telemedicine needs. A pilot study based on twenty patients of trauma has been presented to highlight the limited plastic surgery experience of a doctor in the serious cases. Some outcomes of the work are introduction of user-friendly technology, clinically appropriate telemedicine applications, well trained and professional telemedicine users, etc. 3. USED METHODOLOGY Virtual telemedicine is replacing the physician in telemedicine with a virtual physician. Telemedicine is designed for remote and rural areas [12] whereas virtual telemedicine can be used in both rural and urban areas. In conventional telemedicine, there are simply two nodes: Figure-3.2: A virtual telemedicine system patient and doctor. Patient communicates with the doctor through some telecommunication medium; telephone, e- The time constraint of conventional telemedicine system mail, internet, video-conferencing, etc. A simple is typically longer. An idea of virtusal telemedicine has representation of a conventional telemedicine system has been presented to cover up this time constraint and make been shown in figure 3.1. telemedicine more effective and efficient. Virtual telemedicine is the extension of conventional. A new component ‘medical expert system’ has been deployed in the conventional telemedicine system. This medical expert system is a natural language processing based expert system. In this research this expert system has been named ‘Virtual Medical Expert System’. 3.1. Designed System Architecture This virtual medical expert based system is shown in figure 3.2. This system has robust ability of reading the patient’s symptoms and immediately diagnosing the disease and also prescribing the appropriate medication for the patient. A natural language processing (NLP) based medical expert system is the base of the proposed health care system. The designed rule based expert system has following major components [19]. a- Graphical User Interface Figure - 3.1: A simple telemedicine system b- Medical Expert Knowledge base Major issues that are concerned with the development of c- Medical Inference engine a conventional telemedicine system can be divided into d- Medical Explanation Module four categories [13]. First of all there is need of a. Graphical User Interface infrastructure that is based on hardware, software and connectivity mechanism of multiple nodes (patient and A graphical user interface is a facility for the user to doctor). On the other hand basic medical equipment is interact with the Expert system. A wizard of forms is required at the patient end where a literate person can used to get textual input from the user and then after
  • 4. processing the textual information the output is shown to • Heuristic Knowledge is typically observed or the user in the form of reports. pragmatic knowledge. This type of knowledge is extracted from the factual knowledge i.e. “if patient b- Medical Expert Knowledge base has temperature then it can be chest infection”. MEKB is an intelligent knowledge base that uses Markov c. Medical Expert System Logics (ML) to save domain knowledge. Markov Logic is simple extension to first-order logic. In Markov Logic, This is another very important part of the designed each formula has an additional weight fixed with it [5], in medical expert system. It is the brain of the medical variation of first order logic. In ML, a formula's expert system. The major duty if this part is to make associated weight reflects the strength of a constraint. logical deductions based upon the extracted knowledge The higher weight of a formula represents the greater the from the medical expert knowledge base (MEKB). This difference in log probability and it also satisfies the inference engine not only makes decision but also formula. Use of Markov Logic enables intelligent storage extracts new information on the behalf of provided and retrieval of information using logical connectives and information from MEKB. This new information can also quantifiers. The benefit of using Markov Logics is that become par of the medical inference engine, if required. the queries which even do match up to 80% will also be answered as this is not the case in typical knowledgebase d. Medical Explanation Module that used production rules. This approach will increase the response rate of the knowledge base and makes it This is another very important module of the designed more effective and efficient. system. This module provides the facility of explaining and reasoning of the system to the user. User can make different queries regarding the system domain and Input Text (Patient’s Symptom Report) system. 3.2. Algorithm for Query Processing For diagnosis and treatment of the patient, two techniques Morphological POS Tokenization are used in the proposed system. First and major Analysis Tagging technique to develop virtual telemedicine is “Rule Based Approach” in which is the most efficient way to represent human activity in the form of rules. Used algorithm has Pragmatic Semantic Lexical two major parts. First part has been designed to read the Analysis Analysis Analysis patient’s symptoms of diseases and analyze according to the given knowledge base and diagnose the accurate disease. Second part of the designed algorithm prescribes the suitable medicine to the patient. Following steps are Medical Inference Engine MEKB followed by the algorithm to diagnose a particular disease: Step –I Health care person collects the patient’s disease Diet Details Medication Exercise Details information along with the symptoms of disease and records in the simple English form. Step –II The patient’s case information in the textual Dose form is given to the designed virtual telemedicine system. Side Details Effects Step – III Natural language processing is performed to read the given text and extract the related information. Figure 3.3 - Working of the designed system Used NLP steps to analyze text are [15]: o Tokenization (Separating tokens): The input MEKB handles two types of rules Factual knowledge and sentence is tokenized into complete words Heuristic Knowledge [19]. o Morphology (to identify and analyze morphemes). • Factual Knowledge is the descriptive information. It The input of the previous step is further processed is basic knowledge related to the domain i.e. to identify the complete words and then identify Bacterial causes flue” Or “Dust allergy causes their parts of speech (POS) category. cough”.
  • 5. o Lexical analysis (to identify grammatical types of based wireless internet work or WiFi system supporting the tokens): The POS tagged words are further speed of 1.0 Gbps or above is required. 3G cellular processed to identify their particular role in the technology is also getting very popular these days [16] in sentence and grammatical rules also assist this the field of telehealth. This technology can help out in type of analysis. fast video sharing, video male and video conferencing. On the other side, a telemedicine center at the remote o Semantic analysis (To understand the meanings of area needs basic eequipments [12] i.e. the sentences): Different constituents of a sentence are analyzed here to extract the both implicit and • Virtual telemedicine software explicit meanings of the input text. • Camera (s), lights, projector o Pragmatic analysis (to find out meanings in a • Digital X-Ray System particular context): This is an additional step that • UPS system is used if the meanings of the input text are not • Computer hardware, system and application software clear it is analyzed into its particular context to and accessories make the things more clear and concise. Step –IV Pattern matching of the extracted information is 5. EXPERIMENTS AND RESULTS performed through the medical inference engine with the information in medical expert knowledge base to find out A number of experiments were performed to test the patient’s disease. designed health care system. A medical assistant was Step –V If a match is found then treatment of the disease involved to use the system. A multiple step procedure is is recommended with the appropriate medication and involved to use the designed medical health care system. health instructions. If match is not found then the query The steps are following: is forwarded to the medical expert. 1. Patient Registration In a case, if the virtual medical expert does not find any 2. Patient Record File Generation particular solution of the patient’s query from its 3. Processing User Details knowledge base, rather related to the disease diagnosing 4. Generating Patient Report or prescribing medicine, an automatic email is sent to the medical expert. Medical expert examines the query with Brief description of all these phases with the help of a available facts and makes some decisions and replies the case study has been provided in the later part of the local medical assistant. The designed system also updates section. the Medical Expert Knowledge Base (MEKB) so that if 5.1. Patient Registration the same query comes in future, it may be resolved locally. A patient is needed to register with his personal details i.e. name, age, sex, address, family history, previous cases, etc for using the proposed virtual telemedicine 4. IMPLEMENTATION DETAILS system. Figure 4.1 shows the form that is used to register Rural areas of Pakistan [1] are relatively backward in the patient first. terms of technology. A number of challenges are to face in setting up a system for virtual telemedicine. Some of the major challenges are following: • Budget and financial constraints are more significant [14]. First of all expensive medical equipments are required at the telemedicine centers. High bandwidth for communication is also an expensive solution. • At the site, adequate human resources are required [15] i.e. technicians to implement the proposed virtual Telemedicine system, a medical assistant having medical training to perform basic tests of the patients and some health workers having basic literacy of computer and capable of using computers. A complete infrastructure is required to actually set up Figure 5.1 – Form to register a patient the proposed virtual telemedicine framework. A satellite
  • 6. 5.2. Patient Record File Generation S After registration, the medical expert performs basic tests of a patient to get the reading of temperature, blood pressure, blood group, sugar level and ESG (if required). NP VP Then he records the common symptoms of the patient in the system. Besides these tests, the data i.e. color of Det. Verb NP Noun tongue, color of eyes, heart beat, face color, etc is also captured and is updated in the system. The data form is shown in the figure 4.2 The patient H.V. Adej Noun has high fever Figure 1.0- Parse tree generated for the example There are two rationales for performing the syntactic analysis; to validate the phrases and sentence according to grammatical rules defined by the English language and finding out the semantical constituents of natural language. Moreover, the semantical analysis helps in identifying the main parts of a sentence i.e. object, subject, actions, attributes, etc. [The] [patient] [has] [high] [fever] [.] Subject Verb Object In this step, associations are identified by doing semantic Figure 5.2 – Form to update patient’s status analysis. It is determined in this specified that which actions have been performed by which object and a set of Medical assistant can also use digital stethoscope and attributes belong to which object e.g. in the above electrocardiograph file with ECG recorder or images with example it is extracted that a person is having a high the examination camera [13]. A text file containing the fever. patient’s case details is prepared. 5.4. Generating Patient Report 5.3. Processing User Details Afterwards, the extracted information of the last phase is The input text file containing the patient’s history and matched with the knowledge in MEKB. Inference engine symptoms is given to the designed system foe processing. extracts the desired information and processes the In first step, input is read and tokenized e.g. the output of patient’s symptoms to infer the disease. If disease is a sentence “The patient has high fever.” is found then the respective medication of the disease and additional information i.e. diet and exercise details are [The] [patient] [has] [high] [fever] [.] also provided. The designed system will not only provide the treatment strategy of the disease but also recommends After tokenizing the text, morphological analysis is tests if necessary for the confirmation of the disease. If performed of given text to define the structuring and the tests were recommended by the system to the patient, transformation of the words. POS Tagging is also patient/administrator will have to provide the results of performed to identify different parts of speech e.g. the tests to the system so that system may recommend the [The] [patient] [has] [high] [fever] [.] right treatment of the disease. If the system is not able to answer the patient then an automatic e-mail will be forwarded to the medical expert. The medical expert will Determiner Noun Verb adjective Noun carefully examine the case by consulting all the test reports and data sent by the local medical assistant and After POS tagging, the text is lexically and syntactically diagnosis the disease and also prescribes the appropriate analyzed and a parse tree is generated for semantic medication. When the medical assistant receives the analysis. Figure 5.3 shows the generated parse tree of the response, the medical expert’s opinion is also updated in above example. the knowledge-base of the system.
  • 7. A prescription will be generated after the patient’s data is usability of the Virtual telemedicine will be more useful submitted. If the knowledge base cannot reply then the and beneficial. The experiments were performed on a patient’s data will be emailed to expert. The correctness simulator and it is acceptable that these results may vary of the decision made by the software and the medical when the system will be run real time. In future expert is based on the accuracy of the data captured by enhancements the algorithms is needed to be improved to the medical assistant. The quality and accurateness of the increase the accuracy level of the system. Medical images and video of the patient is also quite important. explanation module is also needed to enhance its usability. To validate the precision and affectivity of the designed system symptom reports of three groups of ten patients References were defined. For each group three reports i.e. easy, average an difficult were generated for each group. The [1] M. Z. Khalid, A. Akbar, A. Kumar , A. Tariq, M. Farooq, symptom reports were carefully prepared and processed [2008] “Using Telemedicine as an Enabler for Antenatal Care for each patient using the designed health care system. in Pakistan”, Proc. 2nd International Conference: E-Medical For correct and wrong diagnosis of a symptom report System, Oct 2008, Tunisia, pp 1-8 various points were given. Table 5.1 shows the details of [2] Tayab Din Memon, BS Chowdhry, AK Baloch, “Design and the results. Implementation of a Telecardiologic System”, MUET, Research Journal, Volume 23, No. 4, Oct 2004. Group 1 Group 2 Group 3 Total % [3] William R., David H., Susan M., Tracy L., Kathryn Pyle, Easy 10/10 9/10 9/10 2.8 93.33 Mark Helfand, [2006] “Telemedicine for the Medicare Population: Update”, AHRQ publication No. 06-E007 Average 9/10 8/10 9/10 2.6 86.66 Difficult 7/10 8/10 8/10 2.3 76.66 [4] Thomas S. Nesbitt, [2007] “Meeting the Health Care Needs of California’s Children: The Role of Telemedicine”, Digital Average Accuracy: 85.5% Opportunity for Youth Issue Brief, Number 3: September 2007 Table 5.1 Virtual Telemedicine Based Healthcare System [5] Albert M. Lai, Jason Nieh, Justin B., [2007] “REPETE2: A Following are some benefits over using the proposed Next Generation Home Telemedicine Architecture”, AMIA framework virtual telemedicine. 2007 Symposium Proceedings, pp 1020-1022 • Improved and immediate access the specialty care [6] Lai AM and Nieh J., [2006] “On the Performance of Wide- • Upgraded emergency medical services Area Thin-Client Computing”, ACM Transaction on Computer Systems. May 2006, p. 215-209 • Reduction in un-necessary duplication of services • Less dependency on the medical expert [7]. Lai AM and Nieh J., [2005] “Web Content Delivery Using • Easier diagnostic consultation Thin-Client Computing”, In: Chanson ST, Xu TJ, editors. • Expanded disease cure education Web Content Delivery. Springer; 2005. pp. 325-346 • More patient health queries • Remote medical consultation [8] Dena Puskin, Barbara and Stuart, [2006] “Telemedicine, • Reduction in health care cost Telehealth, and Health Information Technology”, An ATA Issue Paper, The American Telemedicine Association, May • Automated patient record keeping 2006 6. CONCLUSION & FUTURE WORK [9] Dena S. Puskin, [1995] “Opportunities and challenges to telemedicine in rural America”, Journal of Medical Systems, Virtual Telemedicine is the new concept which actually Volume 19, Number 1 / February, 1995, pp 59-67 works faster than that of the traditional telemedicine systems. An expert system has been deployed in place of [10] E. J. Gomez, F. Del Pozo, M. Hernando, “Telemedicine for a medical expert that has ability to immediate respond. diabetes care: The DIABTel approach towards diabetes telecare”, Informatics for Health and Social Care, Volume This immediate response can help to treat patients in time 21, Issue 4 October 1996 , pages 283 – 295 and more effectively. 90% queries can be entertained locally. The accuracy achieved with the designed system [11] D A Perednia and A. Allen, “Telemedicine Technology and is 85.5%. The Virtual expert system becomes more robust Clinical Applications”, The Journal of the American Medical and intelligent with the passage of time as the Association, 273(6), 8 Feb 1995, pp. 483–88. knowledge-base grows and the level accuracy will also improve. For the under developed and developing countries like Pakistan, Bangladesh, Sri Lanka etc the
  • 8. [12] C A Sable, et al., “Impact of Telemedicine on the Practice Report”, Telemedicine Journal and e-Health, Vol 8, No 2, of Pediatric Cardiology in Community Hospitals”, Pediatrics, 2002. January 2002, Vol. 109, No. 1 pp. e3. [13] Rashid E, Ishtiaq O, Gilani S, et al. “Comparison of store and forward method of teledermatology with face-to-face consultation” J Ayub Med Coll Abbottabad 2003 Apr-Jun;15 (2):34-6. [14] J. Jiehui, Z. Jing, [2007] ‘Remote patient monitoring system for China.’ IEEE Potentials, Vol. 26, Issue 3, pp 26- 29, IEEE, May/June 2007. [15] Bajwa I., Choudhary I. [2006] “A Rule Based Paradigm for Speech Language Context Understanding”, J Donghua University (English Edition) Jun 2006, 23(6), pp.39-42 [16] Hersh W, Helfand M, Wallace J, [2002] “A systematic review of the efficacy of telemedicine for making diagnostic and management decisions”, J Telemed Telecare 8(4):197- 209. [17] Andrew J Diver, Harry Lewis, Derek J Gordon, (2009), “Telemedicine and Trauma Referrals – a Plastic Surgery Pilot Project”, Ulster Med J 2009 78(2), pp. 113-114 [18] Demartines N, Otto U, Mutter D, Labler L, van Weymarn A Vix M, et al. “An evaluation of telemedicine in surgery: tele-diagnosis compared with direct diagnosis” Arch Surg 2000 135(7), pp. 849-53. [19] Shahbaz F., Maqbool F., Razzaq S., Irfan K., Zia T., [2008] “The Role of Medical Expert Systems in Pakistan”, World Academy of Science, Engineering and Technology 2008 Vol. 37 pp. 296-298 [20] Whitten PS, Mair FS, Haycox A, May CR, Williams TL, Hellmich S. et al. “Systematic review of cost effectiveness studies of telemedicine interventions” BMJ 2002; 324(7351) pp. 1434-1437. [21] Yellowlees P. “Successful development of telemedicine systems – seven core principles” J Telemed Telecare 1997; 3(4), pp. 215-222. [22] M P Cutchin, “Virtual Medical Geographies: Conceptualizing Telemedicine and Regionalization”, Progress in Human Geography, 26, 1, 2002, pp. 19–39 [23] G Zahlman and S Laxminarayan, “Special Issue on Telemedical Systems, Guest Editorial”, IEEE Transactions on Information Technology in Biomedicine, Vol 3, Number 2, June 1999. [24]L Kun (1998), “Biomedical Information Technology: Opportunities for the Future”, Proceedings ITAB ‘98, Washington, DC, (Ed S Laxminarayan and E M Tzanakou), IEEE Press. [25] A Lacroix, et al., “International Concerted Action on Collaboration in Telemedicine: Recommendations of the G-8 Global Healthcare Applications Subproject-4, Special