This document describes research on developing an automated system to extract key information from clinical trial literature, such as the hypothesis, sample size, statistical tests used, and conclusions. The system maps extracted phrases to relevant knowledge sources. It was trained and tested on 42 full-text articles about chemotherapy for non-small cell lung cancer, achieving a precision of 86%, recall of 78%, and F-score of 0.82 for classifying sentences. The goal is to utilize this extracted information for quality assessment, meta-analysis, and disease modeling.
College Writing II Synthesis Essay Assignment Summer Semester 2017.docxclarebernice
College Writing II Synthesis Essay Assignment Summer Semester 2017
Directions:
For this assignment you will be writing a synthesis essay. A synthesis is a combination of two or more summaries and sources. In a synthesis essay you will have three paragraphs, an introduction, a synthesis and a conclusion.
In the introduction you will give background information about your topic. You will also include a thesis statement at the end of the introduction paragraph. The thesis statement should describe the goal of your synthesis. (informative or argumentative)
The second paragraph is the synthesis. You will combine two summaries of two different articles on the same topic. You will follow all summary guidelines for these two paragraphs. The synthesis will most likely either argue or inform the reader about the topic.
The conclusion paragraph should summarize the points of your essay and restate the general ideas.
For this essay you will read two research articles on a similar topic to the previous critical review essay as you can use this research in your inquiry paper. You will summarize both articles in two paragraphs and combine the paragraphs for your synthesis. In the synthesis you must include the main ideas of the articles and the author, title, and general idea in the first sentences.
This essay will be three pages long and the first draft and peer review are due June 15. You must turn them in hardcopy in class so you can do a peer review.
Running head: THESIS DRAFT 1
THESIS DRAFT 3Thesis Draft
Katelyn B. Rhodes
D40375299
DeVry University
Point-of-Care Testing (PoCT) has dramatically taken over the field of clinical laboratory testing since it’s introduction approximately 45 years ago. The technologies utilized in PoCT have been refined to deliver accurate and expedient test results and will become even more sensitive and accurate in order to dominate the field of clinical laboratory testing. Furthermore, there will be a dramatic increase in the volume of clinical testing performed outside of the laboratory. New and emerging PoCT technologies utilize sophisticated molecular techniques such as polymerase chain reaction to aid in the treatment of major health problems worldwide, such as sexually transmitted infections (John & Price, 2014).
Historic Timeline
In the early-to-mid 1990’s, bench top analyzers entered the clinical laboratory scene. These analyzers were much smaller than the conventional analyzers being used, and utilized touch-screen PCs for ease of use. For this reason, they were able to be used closer to the patient’s bedside or outside of the laboratory environment. However, at this point in time, laboratory testing results were stored within the device and would have to then be sent to the main central laboratory for analysis.
Technology in the mid-to-late 1990’s permitted analyzers to be much smaller so that they may be easily carried to the patient’s location. Computers also became more ...
38 www.e-enm.org
Endocrinol Metab 2016;31:38-44
http://dx.doi.org/10.3803/EnM.2016.31.1.38
pISSN 2093-596X · eISSN 2093-5978
Review
Article
How to Establish Clinical Prediction Models
Yong-ho Lee1, Heejung Bang2, Dae Jung Kim3
1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Division of Biostatistics, Department
of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA; 3Department of Endocrinology
and Metabolism, Ajou University School of Medicine, Suwon, Korea
A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymp-
tomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education.
Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statisti-
cal analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model develop-
ment and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for de-
veloping and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection;
handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods
for developing clinical prediction models with comparable examples from real practice. After model development and vigorous
validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use
in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading
to active applications in real clinical practice.
Keywords: Clinical prediction model; Development; Validation; Clinical usefulness
INTRODUCTION
Hippocrates emphasized prognosis as a principal component of
medicine [1]. Nevertheless, current medical investigation
mostly focuses on etiological and therapeutic research, rather
than prognostic methods such as the development of clinical
prediction models. Numerous studies have investigated wheth-
er a single variable (e.g., biomarkers or novel clinicobiochemi-
cal parameters) can predict or is associated with certain out-
comes, whereas establishing clinical prediction models by in-
corporating multiple variables is rather complicated, as it re-
quires a multi-step and multivariable/multifactorial approach to
design and analysis [1].
Clinical prediction models can inform patients and their
physicians or other healthcare providers of the patient’s proba-
bility of having or developing a certain disease and help them
with associated decision-making (e.g., facilitating patient-doc-
tor communication based on more objective information). Ap-
Received: 9 January 2016, Revised: 14 ...
College Writing II Synthesis Essay Assignment Summer Semester 2017.docxclarebernice
College Writing II Synthesis Essay Assignment Summer Semester 2017
Directions:
For this assignment you will be writing a synthesis essay. A synthesis is a combination of two or more summaries and sources. In a synthesis essay you will have three paragraphs, an introduction, a synthesis and a conclusion.
In the introduction you will give background information about your topic. You will also include a thesis statement at the end of the introduction paragraph. The thesis statement should describe the goal of your synthesis. (informative or argumentative)
The second paragraph is the synthesis. You will combine two summaries of two different articles on the same topic. You will follow all summary guidelines for these two paragraphs. The synthesis will most likely either argue or inform the reader about the topic.
The conclusion paragraph should summarize the points of your essay and restate the general ideas.
For this essay you will read two research articles on a similar topic to the previous critical review essay as you can use this research in your inquiry paper. You will summarize both articles in two paragraphs and combine the paragraphs for your synthesis. In the synthesis you must include the main ideas of the articles and the author, title, and general idea in the first sentences.
This essay will be three pages long and the first draft and peer review are due June 15. You must turn them in hardcopy in class so you can do a peer review.
Running head: THESIS DRAFT 1
THESIS DRAFT 3Thesis Draft
Katelyn B. Rhodes
D40375299
DeVry University
Point-of-Care Testing (PoCT) has dramatically taken over the field of clinical laboratory testing since it’s introduction approximately 45 years ago. The technologies utilized in PoCT have been refined to deliver accurate and expedient test results and will become even more sensitive and accurate in order to dominate the field of clinical laboratory testing. Furthermore, there will be a dramatic increase in the volume of clinical testing performed outside of the laboratory. New and emerging PoCT technologies utilize sophisticated molecular techniques such as polymerase chain reaction to aid in the treatment of major health problems worldwide, such as sexually transmitted infections (John & Price, 2014).
Historic Timeline
In the early-to-mid 1990’s, bench top analyzers entered the clinical laboratory scene. These analyzers were much smaller than the conventional analyzers being used, and utilized touch-screen PCs for ease of use. For this reason, they were able to be used closer to the patient’s bedside or outside of the laboratory environment. However, at this point in time, laboratory testing results were stored within the device and would have to then be sent to the main central laboratory for analysis.
Technology in the mid-to-late 1990’s permitted analyzers to be much smaller so that they may be easily carried to the patient’s location. Computers also became more ...
38 www.e-enm.org
Endocrinol Metab 2016;31:38-44
http://dx.doi.org/10.3803/EnM.2016.31.1.38
pISSN 2093-596X · eISSN 2093-5978
Review
Article
How to Establish Clinical Prediction Models
Yong-ho Lee1, Heejung Bang2, Dae Jung Kim3
1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Division of Biostatistics, Department
of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA; 3Department of Endocrinology
and Metabolism, Ajou University School of Medicine, Suwon, Korea
A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymp-
tomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education.
Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statisti-
cal analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model develop-
ment and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for de-
veloping and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection;
handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods
for developing clinical prediction models with comparable examples from real practice. After model development and vigorous
validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use
in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading
to active applications in real clinical practice.
Keywords: Clinical prediction model; Development; Validation; Clinical usefulness
INTRODUCTION
Hippocrates emphasized prognosis as a principal component of
medicine [1]. Nevertheless, current medical investigation
mostly focuses on etiological and therapeutic research, rather
than prognostic methods such as the development of clinical
prediction models. Numerous studies have investigated wheth-
er a single variable (e.g., biomarkers or novel clinicobiochemi-
cal parameters) can predict or is associated with certain out-
comes, whereas establishing clinical prediction models by in-
corporating multiple variables is rather complicated, as it re-
quires a multi-step and multivariable/multifactorial approach to
design and analysis [1].
Clinical prediction models can inform patients and their
physicians or other healthcare providers of the patient’s proba-
bility of having or developing a certain disease and help them
with associated decision-making (e.g., facilitating patient-doc-
tor communication based on more objective information). Ap-
Received: 9 January 2016, Revised: 14 ...
184 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009.docxhyacinthshackley2629
184 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9
M E D I C I N E
M edical research studies can be split into fivephases—planning, performance, documenta-
tion, analysis, and publication (1, 2). Aside from finan-
cial, organizational, logistical and personnel questions,
scientific study design is the most important aspect of
study planning. The significance of study design for
subsequent quality, the relability of the conclusions,
and the ability to publish a study are often underestimated
(1). Long before the volunteers are recruited, the study
design has set the points for fulfilling the study objec-
tives. In contrast to errors in the statistical evaluation,
errors in design cannot be corrected after the study has
been completed. This is why the study design must be
laid down carefully before starting and specified in the
study protocol.
The term "study design" is not used consistently in
the scientific literature. The term is often restricted to
the use of a suitable type of study. However, the term
can also mean the overall plan for all procedures in-
volved in the study. If a study is properly planned, the
factors which distort or bias the result of a test procedure
can be minimized (3, 4). We will use the term in a
comprehensive sense in the present article. This will
deal with the following six aspects of study design:
the question to be answered, the study population, the
type of study, the unit of analysis, the measuring tech-
nique, and the calculation of sample size—, on the
basis of selected articles from the international litera-
ture and our own expertise. This is intended to help
the reader to classify and evaluate the results in publi-
cations. Those who plan to perform their own studies
must occupy themselves intensively with the issue of
study design.
Question to be answered
The question to be answered by the research is of
decisive importance for study planning. The research
worker must be clear about the objectives. He must
think very carefully about the question(s) to be
answered by the study. This question must be opera-
tionalized, meaning that it must be converted into a
measurable and evaluable form. This demands an
adequate design and suitable measurement parameters.
A distinction must be made between the main questions
to be answered and secondary questions. The result of
the study should be that open questions are answered
R E V I E W A RT I C L E
Study Design in Medical Research
Part 2 of a Series on the Evaluation of Scientific Publications
Bernd Röhrig, Jean-Baptist du Prel, Maria Blettner
SUMMARY
Background: The scientific value and informativeness of
a medical study are determined to a major extent by the
study design. Errors in study design cannot be corrected
afterwards. Various aspects of study design are discussed
in this article.
Methods: Six essential considerations in the planning and
evaluation of medical research studies are presented and
discussed in the light.
A meta-analysis is the use of statistical methods to summaries the results of the studies. Meta-analyses are conducted to assess the strength of evidence present on a disease and treatment. The results of a meta-analysis can improve precision of estimates of effect, answer questions not posed by the individual studies, settle controversies arising from apparently conflicting studies, and generate new hypotheses. In particular, the examination of heterogeneity is vital to the development of new hypotheses.
Evaluates a meta analysis of family therapy interventions for families facing physical illness.
The slide presentation and article is discussed in greater detail at http://jcoynester.wordpress.com/2013/08/12/interventions-for-the-family-in-chronic-illness-a-meta-analysis-i-like/
Week 5 Lab 3· If you choose to download the software from http.docxcockekeshia
Week 5 Lab 3
· If you choose to download the software from http://www.easyphp.org, use the installation guide provided here to install the EasyPHP.
Lab 3: XAMPP and MySQL Setup
Due Week 5 and worth 75 points
· Install XAMPP and MySQL and take a screen shot that shows the MySQL prompt on your screen. (screen shot optional)
· Research the capabilities of MySQL.
Write a one to two (1-2) page paper in which you:
1. Describe your experiences related to your setup of MySQL. Include any difficulties or issues that you had encountered during the installation.
1. Based on your post-installation research, describe the main capabilities of MySQL.
1. Describe the approach that you would take to go from a conceptual or logical model that you created to the implementation of that database structure in MySQL. Determine the additional information that you will need to implement the database design in a database management system.
Your assignment must follow these formatting requirements:
. Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
. Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
Research studies show thatevidence-based practice(EBP) leads to higher qual-
ity care, improved patient out-
comes, reduced costs, and greater
nurse satisfaction than traditional
approaches to care.1-5 Despite
these favorable findings, many
nurses remain inconsistent in their
implementation of evidence-based
care. Moreover, some nurses,
whose education predates the in-
clusion of EBP in the nursing cur-
riculum, still lack the computer
and Internet search skills neces-
sary to implement these practices.
As a result, misconceptions about
EBP—that it’s too difficult or too
time-consuming—continue to
flourish.
In the first article in this series
(“Igniting a Spirit of Inquiry: An
Essential Foundation for Evidence-
Based Practice,” November 2009),
we described EBP as a problem-
solving approach to the delivery
of health care that integrates the
best evidence from well-designed
studies and patient care data,
and combines it with patient
preferences and values and nurse
expertise. We also addressed the
contribution of EBP to improved
care and patient outcomes, de-
scribed barriers to EBP as well as
factors facilitating its implementa-
tion, and discussed strategies for
igniting a spirit of inquiry in clin-
ical practice, which is the founda-
tion of EBP, referred to as Step
Zero. (Editor’s note: although
EBP has seven steps, they are
numbered zero to six.) In this
article, we offer a brief overview
of the multistep EBP process.
Future articles will elaborate on
each of the EBP steps, using
the context provided by the
Cas.
How Randomized Controlled Trials are Used in Meta-Analysis Pubrica
Randomized Controlled Trials (RCTs) are a commonly used research design in medical and scientific studies to assess the effectiveness of interventions or treatments. Meta-analysis, on the other hand, is a statistical technique used to combine and analyze the results of multiple studies on a particular topic to draw more robust conclusions.
Continue reading @ https://pubrica.com/academy/meta-analysis/how-randomized-controlled-trials-are-used-in-meta-analysis/
For all your research assistance visit us @ https://pubrica.com/services/research-services/
Application Evaluation Project Part 1 Evaluation Plan FocusTec.docxalfredai53p
Application: Evaluation Project Part 1: Evaluation Plan Focus
Technology increases human effectiveness. Using a lever, you can move an object several times your size. In an airplane, you can move exponentially faster than on foot. Using the Internet, you can access information much more quickly than at a library. What possibilities like this exist in the nursing field? What health information technologies can amplify your impact as a nurse far more than ever before? In this Evaluation Project, you will have the opportunity to answer these questions.
Because of the great differences between HIT systems and different goals of an evaluation, there is no one-size-fits-all evaluation plan. Different technologies require different evaluation methods. Consequently, in this part of your Evaluation Project, you will conduct research on how system implementations similar to the one you select have been previously evaluated. After exploring similar system implementations, you will select one research goal and viewpoint to use in the evaluation.
Read the following three scenarios, and select the one that is of most interest to you:
Scenario 1:
Your hospital is implementing a new unified acute and ambulatory Electronic Health Record (EHR) system through which patient care documentation will occur. Interdisciplinary assessment forms (including nursing), clinical decision support, and medical notes will be documented in this system. The implementation of the system is anticipated to improve the hospital’s performance in a multitude of areas. In particular, it is hoped that the use of the EHR system will reduce the rate of patient safety events, improve the quality of care, deter sentinel events, reduce patient readmissions, and impact spending. The implementation of the EHR system is also intended to fulfill the “Meaningful Use” requirements stipulated in the Health Information Technology for Economic and Clinical Health (HITECH) Act. As the hospital’s lead nurse informaticist, you have been tasked with planning the evaluation of the EHR implementation.
Scenario 2:
As the lead nurse informaticist in your hospital, you have been given the task of planning an evaluation for a soon-to-be launched computerized provider order entry (CPOE) system. The CPOE system is designed to replace conventional methods of placing medication, laboratory, admission, referral, and radiology orders. CPOE systems enable health care providers to electronically specify orders, rather than rely on paper prescriptions, telephone calls, and faxes. The intended goal of a CPOE system is to improve safety by ensuring that orders are easily comprehensible through the use of evidence-based order sets. In addition, the CPOE system has the potential for improving workflow by avoiding duplicate orders and reducing the steps between those who place medical orders and their recipients.
Scenario 3:
You are the lead nurse informaticist in a large urban hospital that has recently implemented a new .
How to structure your table for systematic review and meta analysis – PubricaPubrica
According to the, a systematic review is "a scholarly method in which all empirical evidence that meets pre-specified eligibility requirements is gathered to address a particular research question."
Continue Reading: https://bit.ly/3AeFIYY
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...CSCJournals
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
Pubrica's team of researchers and authors develop Scientific and medical research papers that can act as an indispensable tool to the practitioner/authors. Here is how we help.
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxchristinemaritza
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence
Mollie R. Cummins
Ginette A. Pepper
Susan D. Horn
The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies.
Objectives
At the completion of this chapter the reader will be prepared to:
1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs
2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically
3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments
Key Terms
Comparative effectiveness research, 69
Confusion matrix, 62
Data mining, 61
Knowledge discovery and data mining (KDDM), 56
Machine learning, 56
Natural language processing (NLP), 58
Practice-based evidence (PBE), 56
Preprocessing, 56
Abstract
The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs).
Introduction
Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows:
•Are treatments used in daily practice associated with intended outcomes?
•Can we predict adverse events in time to prevent or ameliorate them?
•What treatments work best for which patients?
•With limited financial resources, what are the best interventions to use for specific types of patients?
•What types of indi ...
· Reflect on the four peer-reviewed articles you critically apprai.docxVannaJoy20
· Reflect on the four peer-reviewed articles you critically appraised in Module 4, related to your clinical topic of interest and PICOT.
· Reflect on your current healthcare organization and think about potential opportunities for evidence-based change, using your topic of interest and PICOT as the basis for your reflection.
· Consider the best method of disseminating the results of your presentation to an audience.
The Assignment: (Evidence-Based Project)
Part 4: Recommending an Evidence-Based Practice Change
Create an 8- to 9-slide
narrated PowerPoint presentation in which you do the following:
· Briefly describe your healthcare organization, including its culture and readiness for change. (You may opt to keep various elements of this anonymous, such as your company name.)
· Describe the current problem or opportunity for change. Include in this description the circumstances surrounding the need for change, the scope of the issue, the stakeholders involved, and the risks associated with change implementation in general.
· Propose an evidence-based idea for a change in practice using an EBP approach to decision making. Note that you may find further research needs to be conducted if sufficient evidence is not discovered.
· Describe your plan for knowledge transfer of this change, including knowledge creation, dissemination, and organizational adoption and implementation.
· Explain how you would disseminate the results of your project to an audience. Provide a rationale for why you selected this dissemination strategy.
· Describe the measurable outcomes you hope to achieve with the implementation of this evidence-based change.
· Be sure to provide APA citations of the supporting evidence-based peer reviewed articles you selected to support your thinking.
· Add a lessons learned section that includes the following:
· A summary of the critical appraisal of the peer-reviewed articles you previously submitted
· An explanation about what you learned from completing the Evaluation Table within the Critical Appraisal Tool Worksheet Template (1-3 slides)
Zeinab Hazime
Nurs 6052
10/16/2022
Evaluation Table
Use this document to complete the
evaluation table requirement of the Module 4 Assessment,
Evidence-Based Project, Part 3A: Critical Appraisal of Research
Full
APA formatted citation of selected article.
Article #1
Article #2
Article #3
Article #4
Abraham, J., Kitsiou, S., Meng, A., Burton, S., Vatani, H., & Kannampallil, T.
(2020). Effects of CPOE-based medication ordering on outcomes: an overview of systematic reviews.
BMJ Quality & Safety, 29(10), 1-2.
Alanazi, A. (2020). The effect of computerized physician order entry on mortality rates in pediatric and neonatal care setting: Meta-analysis.
Informatics in Medicine
Unlocked, 19, 100308. https.
184 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009.docxhyacinthshackley2629
184 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9
M E D I C I N E
M edical research studies can be split into fivephases—planning, performance, documenta-
tion, analysis, and publication (1, 2). Aside from finan-
cial, organizational, logistical and personnel questions,
scientific study design is the most important aspect of
study planning. The significance of study design for
subsequent quality, the relability of the conclusions,
and the ability to publish a study are often underestimated
(1). Long before the volunteers are recruited, the study
design has set the points for fulfilling the study objec-
tives. In contrast to errors in the statistical evaluation,
errors in design cannot be corrected after the study has
been completed. This is why the study design must be
laid down carefully before starting and specified in the
study protocol.
The term "study design" is not used consistently in
the scientific literature. The term is often restricted to
the use of a suitable type of study. However, the term
can also mean the overall plan for all procedures in-
volved in the study. If a study is properly planned, the
factors which distort or bias the result of a test procedure
can be minimized (3, 4). We will use the term in a
comprehensive sense in the present article. This will
deal with the following six aspects of study design:
the question to be answered, the study population, the
type of study, the unit of analysis, the measuring tech-
nique, and the calculation of sample size—, on the
basis of selected articles from the international litera-
ture and our own expertise. This is intended to help
the reader to classify and evaluate the results in publi-
cations. Those who plan to perform their own studies
must occupy themselves intensively with the issue of
study design.
Question to be answered
The question to be answered by the research is of
decisive importance for study planning. The research
worker must be clear about the objectives. He must
think very carefully about the question(s) to be
answered by the study. This question must be opera-
tionalized, meaning that it must be converted into a
measurable and evaluable form. This demands an
adequate design and suitable measurement parameters.
A distinction must be made between the main questions
to be answered and secondary questions. The result of
the study should be that open questions are answered
R E V I E W A RT I C L E
Study Design in Medical Research
Part 2 of a Series on the Evaluation of Scientific Publications
Bernd Röhrig, Jean-Baptist du Prel, Maria Blettner
SUMMARY
Background: The scientific value and informativeness of
a medical study are determined to a major extent by the
study design. Errors in study design cannot be corrected
afterwards. Various aspects of study design are discussed
in this article.
Methods: Six essential considerations in the planning and
evaluation of medical research studies are presented and
discussed in the light.
A meta-analysis is the use of statistical methods to summaries the results of the studies. Meta-analyses are conducted to assess the strength of evidence present on a disease and treatment. The results of a meta-analysis can improve precision of estimates of effect, answer questions not posed by the individual studies, settle controversies arising from apparently conflicting studies, and generate new hypotheses. In particular, the examination of heterogeneity is vital to the development of new hypotheses.
Evaluates a meta analysis of family therapy interventions for families facing physical illness.
The slide presentation and article is discussed in greater detail at http://jcoynester.wordpress.com/2013/08/12/interventions-for-the-family-in-chronic-illness-a-meta-analysis-i-like/
Week 5 Lab 3· If you choose to download the software from http.docxcockekeshia
Week 5 Lab 3
· If you choose to download the software from http://www.easyphp.org, use the installation guide provided here to install the EasyPHP.
Lab 3: XAMPP and MySQL Setup
Due Week 5 and worth 75 points
· Install XAMPP and MySQL and take a screen shot that shows the MySQL prompt on your screen. (screen shot optional)
· Research the capabilities of MySQL.
Write a one to two (1-2) page paper in which you:
1. Describe your experiences related to your setup of MySQL. Include any difficulties or issues that you had encountered during the installation.
1. Based on your post-installation research, describe the main capabilities of MySQL.
1. Describe the approach that you would take to go from a conceptual or logical model that you created to the implementation of that database structure in MySQL. Determine the additional information that you will need to implement the database design in a database management system.
Your assignment must follow these formatting requirements:
. Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
. Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
Research studies show thatevidence-based practice(EBP) leads to higher qual-
ity care, improved patient out-
comes, reduced costs, and greater
nurse satisfaction than traditional
approaches to care.1-5 Despite
these favorable findings, many
nurses remain inconsistent in their
implementation of evidence-based
care. Moreover, some nurses,
whose education predates the in-
clusion of EBP in the nursing cur-
riculum, still lack the computer
and Internet search skills neces-
sary to implement these practices.
As a result, misconceptions about
EBP—that it’s too difficult or too
time-consuming—continue to
flourish.
In the first article in this series
(“Igniting a Spirit of Inquiry: An
Essential Foundation for Evidence-
Based Practice,” November 2009),
we described EBP as a problem-
solving approach to the delivery
of health care that integrates the
best evidence from well-designed
studies and patient care data,
and combines it with patient
preferences and values and nurse
expertise. We also addressed the
contribution of EBP to improved
care and patient outcomes, de-
scribed barriers to EBP as well as
factors facilitating its implementa-
tion, and discussed strategies for
igniting a spirit of inquiry in clin-
ical practice, which is the founda-
tion of EBP, referred to as Step
Zero. (Editor’s note: although
EBP has seven steps, they are
numbered zero to six.) In this
article, we offer a brief overview
of the multistep EBP process.
Future articles will elaborate on
each of the EBP steps, using
the context provided by the
Cas.
How Randomized Controlled Trials are Used in Meta-Analysis Pubrica
Randomized Controlled Trials (RCTs) are a commonly used research design in medical and scientific studies to assess the effectiveness of interventions or treatments. Meta-analysis, on the other hand, is a statistical technique used to combine and analyze the results of multiple studies on a particular topic to draw more robust conclusions.
Continue reading @ https://pubrica.com/academy/meta-analysis/how-randomized-controlled-trials-are-used-in-meta-analysis/
For all your research assistance visit us @ https://pubrica.com/services/research-services/
Application Evaluation Project Part 1 Evaluation Plan FocusTec.docxalfredai53p
Application: Evaluation Project Part 1: Evaluation Plan Focus
Technology increases human effectiveness. Using a lever, you can move an object several times your size. In an airplane, you can move exponentially faster than on foot. Using the Internet, you can access information much more quickly than at a library. What possibilities like this exist in the nursing field? What health information technologies can amplify your impact as a nurse far more than ever before? In this Evaluation Project, you will have the opportunity to answer these questions.
Because of the great differences between HIT systems and different goals of an evaluation, there is no one-size-fits-all evaluation plan. Different technologies require different evaluation methods. Consequently, in this part of your Evaluation Project, you will conduct research on how system implementations similar to the one you select have been previously evaluated. After exploring similar system implementations, you will select one research goal and viewpoint to use in the evaluation.
Read the following three scenarios, and select the one that is of most interest to you:
Scenario 1:
Your hospital is implementing a new unified acute and ambulatory Electronic Health Record (EHR) system through which patient care documentation will occur. Interdisciplinary assessment forms (including nursing), clinical decision support, and medical notes will be documented in this system. The implementation of the system is anticipated to improve the hospital’s performance in a multitude of areas. In particular, it is hoped that the use of the EHR system will reduce the rate of patient safety events, improve the quality of care, deter sentinel events, reduce patient readmissions, and impact spending. The implementation of the EHR system is also intended to fulfill the “Meaningful Use” requirements stipulated in the Health Information Technology for Economic and Clinical Health (HITECH) Act. As the hospital’s lead nurse informaticist, you have been tasked with planning the evaluation of the EHR implementation.
Scenario 2:
As the lead nurse informaticist in your hospital, you have been given the task of planning an evaluation for a soon-to-be launched computerized provider order entry (CPOE) system. The CPOE system is designed to replace conventional methods of placing medication, laboratory, admission, referral, and radiology orders. CPOE systems enable health care providers to electronically specify orders, rather than rely on paper prescriptions, telephone calls, and faxes. The intended goal of a CPOE system is to improve safety by ensuring that orders are easily comprehensible through the use of evidence-based order sets. In addition, the CPOE system has the potential for improving workflow by avoiding duplicate orders and reducing the steps between those who place medical orders and their recipients.
Scenario 3:
You are the lead nurse informaticist in a large urban hospital that has recently implemented a new .
How to structure your table for systematic review and meta analysis – PubricaPubrica
According to the, a systematic review is "a scholarly method in which all empirical evidence that meets pre-specified eligibility requirements is gathered to address a particular research question."
Continue Reading: https://bit.ly/3AeFIYY
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...CSCJournals
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
Pubrica's team of researchers and authors develop Scientific and medical research papers that can act as an indispensable tool to the practitioner/authors. Here is how we help.
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxchristinemaritza
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence
Mollie R. Cummins
Ginette A. Pepper
Susan D. Horn
The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies.
Objectives
At the completion of this chapter the reader will be prepared to:
1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs
2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically
3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments
Key Terms
Comparative effectiveness research, 69
Confusion matrix, 62
Data mining, 61
Knowledge discovery and data mining (KDDM), 56
Machine learning, 56
Natural language processing (NLP), 58
Practice-based evidence (PBE), 56
Preprocessing, 56
Abstract
The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs).
Introduction
Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows:
•Are treatments used in daily practice associated with intended outcomes?
•Can we predict adverse events in time to prevent or ameliorate them?
•What treatments work best for which patients?
•With limited financial resources, what are the best interventions to use for specific types of patients?
•What types of indi ...
· Reflect on the four peer-reviewed articles you critically apprai.docxVannaJoy20
· Reflect on the four peer-reviewed articles you critically appraised in Module 4, related to your clinical topic of interest and PICOT.
· Reflect on your current healthcare organization and think about potential opportunities for evidence-based change, using your topic of interest and PICOT as the basis for your reflection.
· Consider the best method of disseminating the results of your presentation to an audience.
The Assignment: (Evidence-Based Project)
Part 4: Recommending an Evidence-Based Practice Change
Create an 8- to 9-slide
narrated PowerPoint presentation in which you do the following:
· Briefly describe your healthcare organization, including its culture and readiness for change. (You may opt to keep various elements of this anonymous, such as your company name.)
· Describe the current problem or opportunity for change. Include in this description the circumstances surrounding the need for change, the scope of the issue, the stakeholders involved, and the risks associated with change implementation in general.
· Propose an evidence-based idea for a change in practice using an EBP approach to decision making. Note that you may find further research needs to be conducted if sufficient evidence is not discovered.
· Describe your plan for knowledge transfer of this change, including knowledge creation, dissemination, and organizational adoption and implementation.
· Explain how you would disseminate the results of your project to an audience. Provide a rationale for why you selected this dissemination strategy.
· Describe the measurable outcomes you hope to achieve with the implementation of this evidence-based change.
· Be sure to provide APA citations of the supporting evidence-based peer reviewed articles you selected to support your thinking.
· Add a lessons learned section that includes the following:
· A summary of the critical appraisal of the peer-reviewed articles you previously submitted
· An explanation about what you learned from completing the Evaluation Table within the Critical Appraisal Tool Worksheet Template (1-3 slides)
Zeinab Hazime
Nurs 6052
10/16/2022
Evaluation Table
Use this document to complete the
evaluation table requirement of the Module 4 Assessment,
Evidence-Based Project, Part 3A: Critical Appraisal of Research
Full
APA formatted citation of selected article.
Article #1
Article #2
Article #3
Article #4
Abraham, J., Kitsiou, S., Meng, A., Burton, S., Vatani, H., & Kannampallil, T.
(2020). Effects of CPOE-based medication ordering on outcomes: an overview of systematic reviews.
BMJ Quality & Safety, 29(10), 1-2.
Alanazi, A. (2020). The effect of computerized physician order entry on mortality rates in pediatric and neonatal care setting: Meta-analysis.
Informatics in Medicine
Unlocked, 19, 100308. https.
Similar to Automated Extraction Of Reported Statistical Analyses Towards A Logical Representation Of Clinical Trial Literature (20)
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Automated Extraction Of Reported Statistical Analyses Towards A Logical Representation Of Clinical Trial Literature
1. Automated Extraction of Reported Statistical Analyses: Towards a Logical
Representation of Clinical Trial Literature
William Hsu1
, PhD, William Speier1,2
, MS, Ricky K. Taira1
, PhD
1
Medical Imaging Informatics Group, Dept of Radiological Sciences
2
Biomedical Engineering Interdepartmental Program
University of California, Los Angeles, CA
Abstract
Randomized controlled trials are an important source of evidence for guiding clinical decisions when treating a
patient. However, given the large number of studies and their variability in quality, determining how to summarize
reported results and formalize them as part of practice guidelines continues to be a challenge. We have developed a
set of information extraction and annotation tools to automate the identification of key information from papers
related to the hypothesis, sample size, statistical test, confidence interval, significance level, and conclusions. We
adapted the Automated Sequence Annotation Pipeline to map extracted phrases to relevant knowledge sources. We
trained and tested our system on a corpus of 42 full-text articles related to chemotherapy of non-small cell lung
cancer. On our test set of 7 papers, we obtained an overall precision of 86%, recall of 78%, and an F-score of 0.82
for classifying sentences. This work represents our efforts towards utilizing this information for quality assessment,
meta-analysis, and modeling.
Introduction
Randomized controlled trials (RCT) represent the most reliable source for elucidating causal relationships between
treatments and outcomes by enforcing strict constraints on the study population and methodology. RCTs represent a
significant source of evidence with over 26,000 papers being indexed on PubMed in 2011 alone1
. While a
significant amount of time and monetary resources have been spent conducting these trials, translating the derived
information into knowledge that can be applied at the patient bedside remains a significant challenge. One important
issue is the difficulty in placing the results of a single trial in the context of a global picture that incorporates all
studies conducted for a given disease. A single study may contribute one causal relationship that needs to somehow
integrate with a broader understanding of the complex causal chain and interactions of a disease process. Currently,
no methodical approach exists for creating a knowledge source that integrates evidence from papers in a principled
manner. Another issue relates to the inconsistent quality of conclusions being reported in literature. Ioannidis [1]
argues that false findings are becoming more prevalent given the misuse of significance testing, inherent biases in
the study population, and lack of statistical power. Several authors have also noted significant increases in retraction
rates over the last decade [2, 3]. While journal editors and reviewers do their best to filter out studies with poorly
described results or faulty experimental design, erroneous conclusions due to faulty statistical analyses continue to
elude the review process and become published [4]. Assessing the validity and quality of the presented evidence
requires the consideration of what variables are measured, how they are measured, what constraints were imposed
on the experiment, estimated distributions of the data, and choice of statistical test and significance value. This
information is primarily published as a free-text communication as part of the results section of a paper, which
compounds the problem because free-text is inherently ambiguous and fraught with imprecision. Some papers
summarize the results in a table, but the reader still needs to refer to the free-text to understand the content of what is
being presented. Ultimately, the burden is on the reader to assess, weigh, and utilize available evidence to determine
the best course of action for a patient’s condition.
The overall objective of this work is to develop an automated pipeline that extracts information related to the
statistical analysis from full-text RCT literature, mapping this information to a logical data model. Towards this
objective, three goals are defined: 1) transform free-text descriptions of the hypothesis, statistical analysis, and
clinical applicability into a logical representation; 2) build modules to identify and annotate variables, attributes,
values, and their relationships from sentences; and 3) develop a workstation to assist in the validation of the
generated results. This work is complementary to the multitude of prior and current efforts to create standardized
reporting of clinical trial results: Global Trial Bank [5], a standardization of how clinical trial protocols are reported,
1
Based on http://dan.corlan.net/medline-trend.html using query ‘randomized and controlled and trial’
350
2. Consolidated Standards of Reporting Trials (CONSORT) statement [6], which defines a set of guidelines to aid RCT
authors in deciding on what to report, ClinicalTrials.gov [7], a repository of semi-structured and standardized
protocols of clinical trials, NeuroScholar [8], a framework for integrating neurology evidence captured from
multiple papers, and EliXR [9], an automated system for extracting temporal constraints from eligibility criteria.
Despite these efforts, little progress has been made towards formalizing the representation of statistical analyses
reported in papers. While many systems attempt to summarize results at a high level (i.e., classifying whether a
sentence is pertinent to the result), few attempt to capture information from papers at a level of detail necessary to
facilitate Bayesian analysis. Our system is a step towards automating the identification of key reported statistical
findings that would contribute to the development of a Bayesian model of a complex disease.
Background
Evidence-based medicine (EBM) requires clinicians to make “conscientious, explicit, and judicious use of the
current best evidence” in their everyday practice [10]. However, understanding how to apply EBM to answer a
clinical question may not be straightforward. One mnemonic developed to assist in applying EBM is: Patient,
Intervention, Comparison, and Outcome (PICO) [11]. Physicians are first asked to characterize the individual or
population being examined and are then asked to define the intervention or therapy being considered. Next, they are
asked to consider alternative treatments and the desired clinical outcome. To facilitate the utilization of PICO,
several groups have examined how to divide a clinical question into its component parts and then apply machine-
learning techniques to perform information retrieval to answer the question [12]. Researchers have studied the task
of automating the extraction of various trial characteristics such as eligibility criteria, sample size, enrollment dates,
experimental and control treatment: Hansen et al [13] describe an approach for extracting the number of trial
participants from the abstract by classifying each sentence using a binary supervised classification based on a
support vector machine algorithm achieving an F-score of 0.86. Chung et al [14] utilize conditional random fields to
classify abstract sentences as being related to either intervention or outcome, achieving an accuracy of 95% and F-
score from 0.77 to 0.85 depending on task. Boudin et al [15] use an ensemble approach to classify PICO elements,
achieving an F-score of 0.863 for participant-related sentences, 0.67 for interventions, and 0.566 for outcomes.
While these initial efforts have demonstrated the ability to automatically classify information from literature to
facilitate information retrieval, several limitations exist. First, these works focused on processing Medline abstracts,
but as Blake [16] notes, only 8% of scientific claims made in a paper are done so in the abstract, emphasizing the
need to examine the entire paper rather than relying on the abstract alone. Second, while sentence classification is
useful to assist in identifying relevant papers, interpreting this information and determining the quality of the
information is still left to the reader. None of these works have developed a data model for the information being
captured to ensure that all necessary contexts for interpreting the information are captured. Finally, most works
remain at the sentence level; contents of these sentences such as variables are not explicitly characterized and linked
with the analyses performed. In this paper, we attempt to not only classify sentences related to the statistical
analyses, but also characterize the values reported in these sentences to populate the data model. This allows the
computer to assist in assessing the validity of reported information and enables this information to be used for meta-
analysis and probabilistic disease modeling. In the following sections, we describe the development of our logical
representation for capturing reported hypothesis, statistical analyses, and conclusions. We then present our
framework for extracting and annotating information from RCT papers.
We demonstrate our system in the domain of non-small cell lung cancer (NSCLC), which is the most common form
of lung cancer. NSCLC was chosen as the domain given its phenomenological complexity, the numerous physical
properties that have been reported (e.g., radiologic, genetic, molecular pathways, clinical findings), and the
numerous interventions that have been used to treat the disease.
Modeling Published Literature
This project is a step towards our overall objective to develop a process model that formally captures key
information from full-text RCT papers such the hypothesis, experimental design, data collection process, analyses
performed, and conclusions drawn. The goal is to create an expressive, robust, and flexible system for capturing all
forms of evidence reported in scientific literature. Ultimately, we desire a representation that permits: 1) traditional
information retrieval tasks such as returning studies based on primary outcome measures; 2) critical appraisal of the
paper by clearly delineating reported (and missing) information; and 3) aggregation of results from multiple trials by
accounting for differences between study populations. In this paper, we focus primarily on creating a logical
representation of the statistical analysis and how it relates to the study hypothesis and clinical conclusions. The
representation is comprised of seven classes: 1) paper (e.g., author names, article title, MeSH terms), 2) hypothesis
351
3. (i.e., statement conveying the objectives of the study), 3) arm (e.g., description of each arm of the study, sample size
of each participant arm/group) 4) statistical method (e.g., the hypothesis test used, power level); 5) result (e.g.,
statement describing statistical significance, p-level); 6) interpretation (any generalizable statements based on study
results that can be applied to clinical practice); and 7) variable (e.g., outcome measure). Figure 1 depicts the logical
representation of the aforementioned classes using an entity-relationship diagram.
Figure 1: The logical representation depicted as an entity-relationship diagram. Relationships between classes are
denoted as 1:1 (one-to-one), 1:n (one-to-many), or n:n (many-to-many).
System Architecture
Figure 2: The overall architecture of the system. The user provides the input (PubMedCentral identifier, PMCID)
and reviews the output through the validation workstation. The dotted lines represent components that are
implemented in the Automated Sequence Annotation Pipeline. Grey rectangles represent annotators implemented as
unique plans; the dark gray rectangle represents an annotator implemented as both a plan and agent.
The system process and information flow are summarized in Figure 2. Briefly, the process involves retrieving the
body of the paper, identifying relevant sentences and variables, annotating variables and incorporating other
352
4. metadata, and mapping the extracted and annotated information to the logical representation. A validation
workstation has been implemented that allows users to view the paper with identified sentences and annotations
overlaid. The following sections describe each component in detail.
Document corpus
The process begins when a user selects a paper of interest though the validation tool. As it would be an intractable
task to consider all NSCLC-related literature, we limited our initial analysis to full-text papers of RCT studies
related to NSCLC and chemotherapy treatments. Utilizing the Open Access subset of PubMedCentral, we performed
an initial search of relevant papers using keywords “non-small cell lung cancer OR NSCLC”, “chemotherapy”,
“randomized controlled trial”, and “NOT review”, resulting in 51 papers. The abstract of each paper was then
manually inspected to ensure that the paper was indeed reporting results of an RCT study. A total of 42 papers were
considered. The corpus was then split into two parts: 35 papers (~80%) were used to create our representation and
classifiers and the remaining 7 papers (~20%) were set aside for the evaluation.
Automated Sequence Annotation Pipeline
The automated sequence annotation pipeline (ASAP) provides an interface for querying biomedical knowledge
sources and integrating the results [17]. The system provides two methods for accessing this data: 1) plans, which
wrap external data sources accessed through the web, and 2) agents, which create a local copy of a data source that
is updated periodically. Users can then create custom plans that successively query these data sources and link their
results in a pipeline. Although ASAP was originally designed for genetic sequencing data, the framework is flexible
enough to extend to other clinical or research data sources. Custom plans and agents are written in Perl and
integrated into the ASAP project. New plans are then able to access databases created by agents and also have the
freedom to call other existing plans. Programmatic interaction with ASAP is achieved through integration with the
distributed annotation system (DAS) [18]. ASAP can be queried using standard web request protocols, automatically
creating and executing the desired job. Output values and intermediate files generated from querying external
sources are encoded in eXtensible Markup Language (XML) based on the DAS protocol [19].
In this work, custom plans and agents for ASAP have been written to retrieve information from data sources such as
PubMedCentral and map extracted terms to relevant databases and biomedical ontologies. Depending on the type of
research article being analyzed, different plans can be executed dynamically. For example, a paper on the molecular
mechanisms of a chemotherapy drug would be mapped to knowledge sources such as Gene Ontology versus a paper
on the imaging correlates of treatment response, which would be mapped to sources such as RadLex. Here, we
demonstrate ASAP’s ability to serialize multiple plans into a pipeline: the input (e.g., PMCID) is passed to the
parent plan, which references additional plans to perform the requested processing steps; the results of each
referenced plan are then aggregated by the parent plan and outputted as a uniform XML representation.
Document retrieval
We have written an ASAP plan that interfaces with the PubMedCentral Open Access web service: given a PMCID,
the plan submits a request to the web service and receives a response with information about the paper encoded in
XML format. While the XML contains information about the entire paper (e.g., text, tables, references), for the
scope of this work, only text related to the body of the paper is examined.
Sentence classification
Once the XML representing the full-text paper is returned, a second ASAP plan is executed that calls a web service
for our classification module. The module is built upon the Unstructured Information Management Application
(UIMA) framework originally developed by IBM [20] and wrapped as a servlet. UIMA is becoming broadly utilized
for natural language processing tasks in the biomedical domain, having recently been implemented in systems such
as Mayo Clinic’s cTAKES [21]. The classification module is comprised of a sentence boundary detector, regular
expressions, parts-of-speech tagger, and a dictionary lookup module. To address the variability in how papers are
organized, a rule-based approach is used to reclassify the original headings into one of five section categories
defined by CONSORT: abstract, introduction, methods, results, and discussion. Each section is then further
tokenized into sentences. A set of regular expressions is used to further classify sentences into specific topics based
on their content. For example, a subset of sentences in the abstract or introduction sections can be categorized as
being part of the study objectives or hypotheses. A sentence in the methods section could be categorized as
describing statistical methods. Based on the topic, a sentence may be processed using one or more additional
annotators to extract variables, attributes, and values. For example, sentences categorized under the topic ‘outcomes
353
5. and estimation’ will be processed using regular expression patterns that identify p-values, confidence intervals, and
statistical interpretation (e.g., no significant difference). Other sentences, such as ones categorized under the topic
‘outcomes’ are processed using the OpenNLP implementation of a maximum entropy-based parts-of-speech tagger
to extract noun phrases, which are then tagged for additional processing in the annotation step. The output of the
classification module is encoded in XML and contains the original sentence, section, topic, phrases/values that are
tagged for annotation, and the name of the annotator to execute. A summary of topics and related annotators are
given in Table 1.
Section Topic Annotator
Paper metadata --- Clinical trials annotator (retrieve trial metadata)
Introduction Hypothesis or objective ---
Methods
Outcomes
Parts of speech tagger (identify noun phrases)
Concept annotator (identify biomedical concepts)
Sample size Regular expression (extract number of patients)
Statistical methods
Parts of speech tagger (identify noun phrases)
Statistics annotator (identify statistical tests)
Results
Outcomes and estimation
Regular expression (extract statistical interpretation, p-value,
confidence intervals, comparison groups)
Harms
Parts of speech tagger (identify noun phrases)
Concept annotator (identify medical problems)
Discussion Generalizability
Regular expression (classify sentences related to clinical
applicability)
Table 1: A summary of section headings and topics. For each topic, one or more annotators is used to parse the
sentence and extract information that is mapped to the logical representation.
Annotation and mapping
The results returned by the sentence classifier are encoded in an XML that specifies the category in which the
sentence or token is classified. ASAP then executes additional plans based on the nature of the sentence or token to
be annotated. For example, if the sentence category is hypothesis, the biomedical concept annotator is executed to
identify potential variables. Below, we elaborate on each of the implemented plans.
Concept annotator. The purpose of the concept annotator is to identify variables from the entire set of noun
phrases. This annotator is implemented as an ASAP plan that interfaces with the National Center for
Biomedical Ontology’s BioPortal web service. Using BioPortal, noun phrases identified in the classification
step are mapped to standardized concepts from one or more biomedical ontologies. Given our domain of
interest, NSCLC, we initially map phrases to the National Cancer Institute Thesaurus (NCIt); however, other
ontologies such as Gene Ontology may be added depending on the nature of the papers being analyzed. The
result of the concept annotator is a list of matched concepts, their concept unique identifiers, and semantic
types.
Statistics annotator. The statistics annotator focuses on matching a noun phrase to a list of analysis techniques,
such as hypothesis testing and survival analysis. This annotator is comprised of three components: an agent, a
local database, and a plan. We created a list of possible statistical analysis methods by manually reviewing
concepts in existing research ontologies such as the Ontology for Biomedical Investigations and Ontology of
Clinical Research. The spreadsheet contains columns representing the first word of the method name, the full
method name, a unique identifier, and a mapping to the source ontology. Using the agent, the spreadsheet is
loaded as a table in the ASAP database. A plan was then written to search the database using the noun phrase as
input using a dictionary lookup approach. Presently, the annotator returns the unique identifier associated with
the statistical method; however, given additional information about each statistical method (e.g., nature of
independent/dependent variables, number of dependent variables), annotations could help determine whether
the appropriate statistical method was used to analyze the data.
Clinical trials annotator. The goal of the clinical trials annotator is to retrieve contextual information about the
study from a public resource such as ClinicalTrials.gov. As of July 2012, over 129,000 trials conducted in 179
countries are indexed on the site. Each trial includes semi-structured information pertaining to study objectives,
patient recruitment, interventions, primary outcomes, and results, whenever available. The annotator is
implemented as an ASAP plan that interfaces with the ClinicalTrials.gov web service. The input for the plan is
354
6. the national clinical trial identifier that is typically provided in the DataBank element of the PubMed XML
and/or abstract text. The output is information about the study encoded in XML.
Results from each plan are combined into an XML schema specified by DAS containing all of the sections,
sentences, and annotations generated by the pipeline.
Validation Workstation
Figure 3: The validation workstation user interface. (A) A search pane provides functions to search for papers of
interest and retrieve selected papers from PubMedCentral. (B) The text body of the paper is presented in the
extraction pane. Results of the ASAP pipeline are shown as highlighted sentences, which can be selected to reveal
extracted values. (C) A results pane presents a structured view of the extracted values.
A validation workstation has been implemented to assist researchers with viewing, annotating, and validating
statistical information extracted from the paper. The workstation, illustrated in Figure 3, consists of the following
features:
1. Search pane (Fig. 3A): The user first searches for a collection of papers in the area of interest (e.g., non-small
lung cancer, chemotherapies). A list of matching PMCIDs are returned and displayed as a selectable list.
2. Extraction pane (Fig. 3B): Once the user selects a specific paper to annotate, the body of the paper appears in
the viewing pane. Metadata drawn from PubMed such as article title, authors, journal name, and MeSH terms
are displayed alongside the selected paper. Manual annotation tools such as highlighting and commenting are
provided.
3. Results pane (Fig. 3C): When the user selects “Annotate”, the ASAP pipeline shown in Figure 2 is executed.
Results are returned, parsed, and presented in the results pane. Extracted values are presented in a tabular view.
Alongside the value is the rule or annotator that was used to extract the information. Users can correct the
extracted values, if needed. This pane reflects the information captured in the logical representation for the
selected paper.
Evaluation
Inter-rater agreement
Two informatics researchers were asked to manually annotate papers from the training set. Both individuals had
previous experience performing systematic reviews of clinical literature and only needed minimal training on the
types of information being sought in the data model. The annotators were asked to identify the minimal number of
sentences that answer the following questions:
355
7. • What are the objectives or hypotheses of the study?
• What are the statistical methods? What are the primary/secondary endpoints? What is the total sample size?
What are the sample sizes for each group? What statistical tests are used? What variables are measured?
Are significance levels and/or confidence intervals reported?
• What conclusions (statistical/clinical significance) are reported about the study?
We examined the overall agreement between the two annotators and obtained a Cohen’s kappa coefficient of 0.89,
indicating that consistency between the two raters overall was achieved. While the vast majority of the paper could
be reliably classified, the clinical conclusions annotation had the least amount of agreement, achieving a kappa score
of 0.12. We believe the poor agreement between the two annotators spawns from the inherent ambiguity in defining
what a conclusion was in the context of a paper. One annotator highlighted any sentence that was in the conclusion
section of the paper while the other highlighted sentences that pertained specifically to the study’s clinical relevance.
Moving forward, we intend to utilize the CONSORT statement’s definition of ‘generalizability’ to guide annotators
with this part of the annotation task. We also plan to involve clinicians as part of the evaluation process to provide
perspective on their information needs while reading scientific literature.
Classification
We performed an evaluation of the sentence classifier using precision, recall, and F-score as measures of
performance. The test set consisted of seven papers with a total of 753 sentences. We primarily evaluated the
sentence classification and value extraction tasks. For sentence classification, we validated that sentences classified
being a hypothesis, statistical method, result, or discussion. For the value extraction task, we present results for
extracting specific values related to the statistical test name and reported significance value. The results of the
classification task are summarized in Table 2.
Topic Example Precision Recall F-Score
Hypothesis
…this phase III trial which was conducted in
order to determine whether the combination of
docetaxel/carboplatin provides any therapeutic
benefit compared to single-agent docetaxel.
83% 91% 0.86
Statistical method
The primary endpoint of the study was to
assess the overall response rate (ORR)...
95% 76% 0.84
Outcomes and
estimation
…the median PFS was 3.33 months (range:
0.2-23.0 months; 95% CI: 2.59-4.07) and 2.60
months (range: 0.5-17.8 months; 95% CI:
1.74-3.46) (p-value = 0.012; Figure 1)…
93% 88% 0.90
Generalizability
The results of the current study demonstrate
that second-line combination treatment with
docetaxel/carboplatin offers a statistically
significant therapeutic benefit compared to
docetaxel monotherapy, in terms of PFS in
patients with NSCLC…
63% 55% 0.59
Table 2: Summary of results after evaluating the test set of seven papers. Sentences in the example column are
drawn from [22].
Our overall precision, recall, and F-score were 86%, 78%, and 0.82, which are comparable to systems performing
similar tasks. While categories such as hypothesis and result had relatively limited number of variations in the way
the information was expressed, the discussion category proved to be most difficult given the large variability seen in
sentence structure and vocabulary used. As previously discussed, manual annotators had poor agreement with
categorizing sentences in this section. Errors generated at the initial stages of the pipeline propagate through the
subsequent tasks; thus, we are exploring robust, non rule-based approaches such as conditional random fields and
support vector machines.
356
8. Annotation
We also started to evaluate how well values are being extracted from sentences. Table 3 provides examples of how
sentences are parsed, annotated, and mapped to the logical representation. Values such as the calculated p- or r-value
and confidence intervals have high precision and recall rates (92% and 95%, respectively), given that the variability
in how these values are expressed is small. On the other hand, capturing the variability in how independent
variables, dependent variables, and hypothesis tests are reported has been challenging. While certain variables such
as outcome measures are well defined (e.g., response rate, overall survival, progression-free survival), less
standardized variables have been difficult to consistently identify (e.g., abbreviations, variations of drug names). The
mapping process identifies a large number of false positives, particularly when common words (e.g., treatment) and
acronyms are present in the sentence. Utilizing a part-of-speech tagger helps narrow the scope of words that are
under consideration. Nevertheless, our current dictionary lookup approach performs poorly and is unable to handle
comparisons between words with subtle differences.
Sentence Class Attribute
From July 2003 to December 2007, 561 patients
were enrolled onto the study… 268 (95%) and 275
patients (98%) received celecoxib and placebo,
respectively.
Arm
Total enrolled: 561
Arm 1: Celecoxib, n=268
Arm 2: Placebo, n=275
Median progression-free survival was 4.5 months
(95% CI, 4.0 to 4.8) for the celecoxib arm and 4.0
months (95% CI, 3.6 to 4.9) for the placebo arm
(hazard ratio [HR], 0.8; 95% CI, 0.6 to 1.1; P =
.25).
Statistical Method
Comparison arm 1: Celecoxib
Comparison arm 2: Placebo
Method name: log rank test
Result
Estimation parameter: Hazard ratio
Estimation value: 0.8
Confidence interval: [0.6, 1.1]
P-value: 0.25
Significance level: 0.05
Variable Progression-free survival
Interpretation Stat significance: Not significant
Table 3: Examples of how sentences from [23] are parsed, annotated, and mapped to the logical representation.
Discussion
We present an automated system for extracting, annotating, and mapping information related to statistical methods
from RCT papers into a logical representation. While the focus of this paper is on representing statistical
information, the overarching goal of our project is to create a logical representation of the entire trial, including the
experimental design, data collection process, analysis methods, and conclusions drawn. Ultimately, we seek to
create a representation that captures the relationships between each section of an RCT paper. For example, the way
patients are included/excluded from the study population will influence the generalizability of the study’s findings in
clinical practice. We wish to capture the dependencies between each section in a process model, where the study
population, interventions, outcome measures, and flow of events are explicitly represented on a timeline. Fragments
of evidence, such as measurements taken at different time points reported in the paper, are captured and associated
with the variables that they measure. Statistical information that has been extracted (e.g., study arms, hypothesis test,
significance level) can be associated with the outcome variables, reported results, and interpreted statistical and/or
clinical significance. A distinction should be drawn in regards to statistical significance and clinical significance
[24]. Even if a hypothesis is found to have statistical significance based on a null hypothesis test that yields a p-
value lower than a predefined significance criterion (e.g., p < 0.05), the finding may not be clinically significant.
Furthermore, the intuition of p-values has been widely debated [25]. Clinical significance is influenced by factors
such as magnitude of effect, prevalence of the disease, practicality of applying such approach in practice, and the
tradeoff between cost and benefit. We believe a logical representation such as the one described helps capture
important context related to a statistically significant finding. This information can facilitate a Bayesian
interpretation of findings, providing a meaningful way for clinicians to judge how reported findings are applicable to
their individual patients.
357
9. Our efforts towards automating the identification of statistical information are complementary to existing efforts to
model the RCT domain. We are standardizing the values that are being inputted into our logical representation using
concepts specified within the Ontology for Biomedical Investigations or Ontology of Clinical Research. Our use of
the ASAP framework demonstrates its flexible architecture for adapting to different domains. We were able to wrap
various knowledge sources and processing tools (i.e., UIMA) as web services with which ASAP could interface. We
have followed a hybrid top-down, bottom-up strategy towards creating our logical representation: we started with a
basic model created from existing ontologies and formalisms of statistical knowledge. Then, we manually examined
the training set, adding new concepts and attributes to the model based on our observations. We acknowledge the
need to expand the breadth of our work into other domains (e.g., all lung cancers) to capture further variations in
reported statistical analysis. We believe the approach described in this paper can be generalized to facilitate the
information extraction of other sections of the paper such as the interventions and experimental protocol.
Acknowledgements
The authors would like to thank Dr. Michael Ochs of the Johns Hopkins University for allowing us to adapt the
ASAP framework for this application. We would also like to acknowledge Dr. James Sayre for his feedback to this
work. This work is supported in part by the National Library of Medicine through grants 5R01LM009961 and
5T15LM007356.
References
1. Ioannidis JPA. Why Most Published Research Findings Are False. PLoS Med. 2005;2(8):e124.
2. Cokol M, Ozbay F, Rodriguez-Esteban R. Retraction rates are on the rise. EMBO Rep. 2008;9(1):2-.
3. Marcovitch H. Is research safe in their hands? BMJ. 2011;342:d284.
4. Harris AHS, Reeder R, Hyun JK. Common statistical and research design problems in manuscripts submitted to
high-impact psychiatry journals: What editors and reviewers want authors to know. Journal of Psychiatric
Research. 2009;43(15):1231-4.
5. Sim I, Detmer DE. Beyond trial registration: a global trial bank for clinical trial reporting. PLoS Med.
2005;2(11):e365.
6. Schulz K, Altman D, Moher D, Group tC. CONSORT 2010 Statement: updated guidelines for reporting parallel
group randomised trials. BMC Medicine. 2010;8(1):18.
7. Zarin DA, Tse T, Williams RJ, Califf RM, Ide NC. The ClinicalTrials.gov Results Database — Update and Key
Issues. N Engl J Med. 2011;364(9):852-60.
8. Burns G, Cheng W-C. Tools for knowledge acquisition within the NeuroScholar system and their application to
anatomical tract-tracing data. Journal of Biomedical Discovery and Collaboration. 2006;1(1):10.
9. Weng C, Wu X, Luo Z, Boland MR, Theodoratos D, Johnson SB. EliXR: an approach to eligibility criteria
extraction and representation. J Am Med Inform Assoc. 2011;18(Suppl 1):i116-24.
10. Sackett DL, Rosenberg WMC, Gray JAM, Haynes RB, Richardson WS. Evidence based medicine: what it is
and what it isn't. BMJ. 1996;312(7023):71-2.
11. Sackett DL. Evidence-based medicine : how to practice and teach EBM. Edinburgh; New York: Churchill
Livingstone; 2000.
12. Huang X, Lin J, Demner-Fushman D. Evaluation of PICO as a knowledge representation for clinical questions.
AMIA Annu Symp Proc. 2006. p. 359-63.
13. Hansen MJ, Rasmussen NØ, Chung G. A method of extracting the number of trial participants from abstracts
describing randomized controlled trials. J Telemed Telecare. 2008;14(7):354-8.
14. Chung G. Sentence retrieval for abstracts of randomized controlled trials. BMC Med Inform Decis Mak.
2009;9(1):10.
15. Boudin F, Nie J-Y, Bartlett J, Grad R, Pluye P, Dawes M. Combining classifiers for robust PICO element
detection. BMC Med Inform Decis Mak. 2010;10(1):29.
16. Catherine B. Beyond genes, proteins, and abstracts: Identifying scientific claims from full-text biomedical
articles. J Biomed Inform. 2010;43(2):173-89.
17. Kossenkov A, Manion FJ, Korotkov E, Moloshok TD, Ochs MF. ASAP: automated sequence annotation
pipeline for web-based updating of sequence information with a local dynamic database. Bioinformatics.
2003;19(5):675-6.
18. Dowell R, Jokerst R, Day A, Eddy S, Stein L. The Distributed Annotation System. BMC Bioinformatics.
2001;2(1):7.
19. Speier W, Ochs MF. Updating annotation with the distributed annotation system and the automated sequence
annotation pipeline. Bioinformatics. Forthcoming.
358
10. 20. Ferrucci D, Lally A. UIMA: an architectural approach to unstructured information processing in the corporate
research environment. Nat Lang Eng. 2004;10(3-4):327-48.
21. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System
(cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010 September 1,
2010;17(5):507-13.
22. Pallis A, Agelaki S, Agelidou A, et al. A randomized phase III study of the docetaxel/carboplatin combination
versus docetaxel single-agent as second line treatment for patients with advanced/metastatic Non-Small Cell
Lung Cancer. BMC Cancer. 2010;10(1):633.
23. Groen HJM, Sietsma H, Vincent A, et al. Randomized, Placebo-Controlled Phase III Study of Docetaxel Plus
Carboplatin With Celecoxib and Cyclooxygenase-2 Expression As a Biomarker for Patients With Advanced
Non–Small-Cell Lung Cancer: The NVALT-4 Study. J Clin Oncol. 2011;29(32):4320-6.
24. Friedman LM. Clinical Significance versus Statistical Significance. Encyclopedia of Biostatistics: John Wiley
& Sons, Ltd; 2005.
25. Goodman SN. Toward evidence-based medical statistics. 1: The P value fallacy. Ann Intern Med.
1999;130(12):995-1004.
359