The diagnostic laboratory has always been a key source of data that informs clinical decisions. Clinical pathology tests generate discrete results with numeric or coded values that can be classified as normal or abnormal. Anatomic pathology analysis results in a report based on visual analysis of tissues. The emerging discipline of data science offers a valuable toolkit to maximize the value of all modalities of laboratory data and to improve the diagnostic and operational functions of a modern lab

1. THE POWER OF DATA SCIENCE
& ANALYTICS IN CLINICAL
LABORATORY
BY
OSAYANDE CHELSEA IMUETINYANOSA
SEMINAR SUPERVISOR:
ADMLS MADUKWE JONATHAN (PhD)
22ND FEBRUARY, 2023
A SEMINAR PRESENTATION IN FULFILMENT
FOR INTERNSHIP IN NATIONAL HOSPITAL,
ABUJA.

2. OUTLINE
● INTRODUCTION
● WHAT IS DATA
● WHAT IS DATA SCIENCE
● FIELDS IN DATA SCIENCE
● WHAT IS DATA ANALYTICS
● DATA SCIENCE TRACKING PROCESS
● ETHICS FOR HEALTH INFORMATION
SYSTEMS
● CONCLUSION
● RECOMMENDATION

3. INTRODUCTION
 The diagnostic laboratory has always been a key source of data that
informs clinical decisions.
 Clinical pathology tests generate discrete results with numeric or coded
values that can be classified as normal or abnormal.
 Anatomic pathology analysis results in a report based on visual analysis of
tissues.
 The emerging discipline of data science offers a valuable toolkit to
maximize the value of all modalities of laboratory data and to improve the
diagnostic and operational functions of a modern lab (Bhavnani et al.,
2016).

4. WHAT IS DATA?
I CAN SOLVE YOUR PROBLEMS!
MAKE A WISH OR DON’T
DATA
Author’s illustration, 2023

5. WHAT IS DATA SCIENCE?
● Data science refers to the combination of computational, statistical and
subject matter expertise necessary to recognize subtle patterns in high
volume, complex data and then to develop predictive models based on
those analyses.
● Biomedical Data Science is the interdisciplinary field that encompasses
the study and pursuit of the effective uses of biomedical data, information,
and knowledge for scientific inquiry, problem-solving, and decision-making,
driven by efforts to improve human health (Zapf et al., 2018).

6. FIELDS IN DATA SCIENCE
Artificial
Intelligence
(AI)
Machine
Learning
Deep
Learning
(Litjens et al., 2016)

7. WHAT IS DATA ANALYTICS?
 Data analytics includes discovering useful information and conclusions to
support decision making from data and includes:
 Using real-world data;
 Visualization;
 Statistical and exploratory analysis
 Machine learning; and
 Communicating results
(Reddy & Aggarwal, 2015).

8. DATA SCIENCE TRACKING PROCESS
DATA MINING
Acquire and
extract data
A
DATA MAINTENANCE
Data warehousing
and cleansing
B
DATA MODELLING
D
DATA ANALYSIS
Predictive analysis,
qualitative analysis
C
DATA
VISUALIZATION
Decision making
E
DATA SCIENCE
(Litjens et al., 2016)

9. DATA MINING: HOW MUCH DATA ARE WE
TALKING ABOUT?
22 federal hospitals
23,640 health facilities
69 notable research institutes
•If 105 hospitals in England
providing pathology services
processes over 1 billion tests
each year, in the same
condition 22 federal hospitals in
Nigeria will process an
estimate of 210 million tests
each year
(Bhavnani et al., 2016).

10. 2022 NHA HISTOPATHOLOGY DATA
2754
(35%)
2501
(32%)
2648
(33%)
HISTOLOGY
CYTOLOGY
IMMUNOHISTOCHEMMISTRY
(NHA HISTOPATHOLOGY LAB, 2023).
NO. OF TEST = 7903

11. TYPES OF LABORATORY DATA
STRUCTURED DATA
• Height
• Weight
• Blood pressure
• Blood type
UNSTRUCTURED DATA
• Physician notes
• X-ray images
• Pathology slides
• Gram stain
(Reddy& Aggarwal, 2015).

12. LABORATORY DATA MAINTENACE
● Efficient, organized, and detailed data maintenance are the cornerstones
of a successful laboratory.
● The complexity of data generated in the modern laboratory setting
presents a significant challenge to these principles of proper record
keeping and data integrity
● Paper and electronic record keeping can be used to facilitate successful
laboratory operations for the scientist conducting basic research (Dirnagl &
Przesdzing, 2016).

13. LI(M)S
● A laboratory information (management)
system (LI(M)S) is a software system
that records, manages, and stores data
for clinical laboratories.
● A LIS has traditionally been most adept
at sending laboratory test orders to lab
instruments, tracking those orders, and
then recording the results, typically to a
searchable database (Park et al., 2012).
SAMPLE LOG
IN
JOB
ASSIGNMENT
TRACKING
PRORESS
DATA
ENTRY
DATA
VALIDATION
REPORTING
RESULTS
SAMPLE IN DATA OUT

14. LABORATORY DATA ANALYSIS
Bartolucci et al., 2016
DESCRIPTIVE
STATISTICS
• Measures of Central
Tendency
• Measures of Variation
DISTRIBUTIONS AND
HYPOTHESIS TESTING
• Confidence Intervals (CI),
• Hypothesis Testing
METHOD VALIDATION
• Accuracy
• Sensitivity, Specificity
(Selectivity)
OUTLIER ANALYSIS
• Grubb Statistic
• Dixon Q-Test for a Single
Outlier
STATISTICAL PROCESS
CONTROL
• Control Charts
• Capability analysis
LIMITS OF CALIBRATION
• Limit Strategies
• Calibration bias

15. DATA ANALYSIS TOOLS
● Microsoft Excel
● SPSS
● Python
● R
● Jupyter Notebook
● Apache Spark
● SAS
● Microsoft Power BI
● Tableau
● KNIME
(Anand et al., 2021).

16. PYTHON
Figure showing the distribution of ages using a cervical cancer dataset shared on
the UC Irvine machine learning data repository (Anand et al., 2021).

17. LABORATORY DATA MODELING
● Data modeling is the process of creating a visual
representation of a software system to illustrate the
data it contains and how it flows.
● Data models help standardize the storage of both
the data and the relationships among data elements
● A popular data model is arranging data into tables.
The tables have columns and rows, each cataloging
an attribute present in the entity (Klann et al., 2016).

18. WHAT ARE THE BEST DATA MODELLING TOOLS?
● The best tools are listed below:
• Elixir Data
• ER/Studio
• DbSchema
• HeidiSQL
• Toad Data Modeler
• ERBuilder
(Anand et al., 2021).

19. DATA VISUALIZATION
● Data visualization is the visual
presentation of data or information.
The goal of data visualization is to
communicate data or information clearly
and effectively to readers. Typically, data
is visualized in the form of a chart,
infographic, diagram or map
● Example is visualizing the prevalence of a
disease using a map (Bhavnani et al.,
2016).

20. MALARIA AROUND THE WORLD
(Bhavnani et al., 2016).

21. LAB RESULTS + MACHINE LEARNING = THE FUTURE!
● Whether it is classifying cell types as cancerous or non-cancerous, or
predicting if an isolated pathogen will be susceptible to a drug of choice,
machine learning is an ideal tool for the job.
● A natural language processing tool might decode the physician’s note and
interpret it as “chest pain, trouble breathing, general fatigue,”
● While a machine learning decision support tool might suggest that these
are symptoms related to hypertension (this diagnosis would also benefit
from structured contextual data like the patient’s height, weight and heart
rate) (Anand et al., 2021).

22. LUNG CANCER DETECTION USING NEURAL NETWORK
(Bhavnani et al., 2016).

23. THE RESEARCH WORLD HAS SHOWED SOME
PROMISES
• Deep learning being applied to the prediction of antibiotic resistance from
meta-genomic data
• Using deep learning techniques to analyze image data for the evidence of
prostrate or breast cancer
• Constitutional Neural Networks can be used to read gram stains
• Classification of haematologic disease using readily available laboratory
blood test results (Donoho, 2017).

24. CONCLUSION
 Biomedical researchers and healthcare engineers are no strangers to big
data.
 The diagnostic laboratory has always been a key source of data that informs
clinical decisions.
 Data science is crucial for efforts to mimic and decode the human brain and
engineer a better healthcare system.

25. RECOMMEDATION
● Hospitals should set up a research team from the lab that will constantly add
to the body of knowledge world wide.
● Data science should be added to undergraduate medical laboratory science
curriculum in order to expose students to the emerging field.
● More medical laboratory scientists should specialize in biomedical data
science in order to fully utilize data generated from the lab

26. REFERENCES
● Anand Nayyar, Lata Gadhavi, Noor Zaman, (2021). Machine learning in healthcare:
review, opportunities and challenges, 2(1): 23-45. https://doi.org/10.1016/B978-0-12-
821229-5.00011-2
● Bartolucci, A. A., Singh, K. P., & Bae, S. (2016). Introduction to Statistical Analysis of
Laboratory Data. doi:10.1002/9781118736890
● Bhavnani, S. P., Mu˜noz, D., & Bagai, A. (2016). Data science in healthcare:
implications for early career investigators. Circulation: Cardiovascular Quality and
Outcomes, 9, 683–687.
● Dirnagl U, Przesdzing I. (2016). A pocket guide to electronic laboratory notebooks in
the academic life sciences;5:2. https://doi.org/10.12688/f1000research.7628.1.
● Donoho, D. (2017). 50 Years of Data Science. Journal of Computational and Graphical
Statistics, 26, 745–766.

27. REFERENCES
● Klann JG, Abend A, Raghavan VA, Mandl KD, Murphy SN. Data interchange using
i2b2. J Am Med Informatics Assoc. 2016;23:909–15.
● Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., van
der Laak, J. (2016). Deep learning as a tool for increased accuracy and efficiency of
histopathological diagnosis. Scientific Reports, 6(1). doi:10.1038/srep26286
● Park, S. L., Pantanowitz, L., Sharma, G., & Parwani, A. V. (2012). Anatomic Pathology
Laboratory Information Systems. Advances In Anatomic Pathology, 19(2), 81–96.
doi:10.1097/pap.0b013e318248b787
● Reddy, C. K., & Aggarwal, C. C. (2015). Healthcare Data Analytics. Chapman and
Hall/CRC.
● Zapf, A., Huebner, M., Rauch, G., & Kieser, M. (2018). What makes a biostatistician?
Statistics in Medicine, 1–7.

28. THANK YOU FOR LISTENING!