About Introduction to Biomedical Data Science

Data science has steadily increased in popularity over the past decade and involves all industries including healthcare and the biomedical sciences. The goal in healthcare is to use data science methods to improve medical quality and safety and reduce costs. 

There is optimism that machine learning and artificial intelligence (AI) will be major drivers of predictive analytics, image, voice and text recognition. Recently, applied AI models have outperformed medical experts at classifying medical images – particularly in cardiology, dermatology, ophthalmology and radiology.

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This textbook was written for anyone in the medical or informatics fields who feels they need a stronger background in data science.  Understanding the textbook content and data exercises do not require programming skills or higher math. Chapter exercises are based on healthcare data and supplemental YouTube videos are available in most chapters. The content begins with spreadsheet tips and tricks and ends with artificial intelligence.

Bill Hersh MD announced in February 2021 that OHSU will launch a new course on applied data science and machine learning for health and clinical informatics students using this textbook. 

A special thanks to Ann Yoshihashi MD for her help with the publication of this textbook. If you have any questions, corrections or suggestions please use the Contact Us page. 

About the 7th Edition

Below you will find a list of the textbook authors and a detailed table of contents.

1. Authors

Brenda Griffith

• Technical Writer,Data.World
• Austin, TX

Robert Hoyt MD, FACP, ABPM-CI, FAMIA

• Associate Clinical Professor
• Department of Internal Medicine
• Virginia Commonwealth University,Richmond, VA

David Hurwitz MD, FACP, ABPM-CI

• Associate CMIO
• Allscripts Healthcare Solutions
• Chicago, IL

Madhurima Kaushal MS

• Bioinformatics
• Washington University at St. Louis, School of Medicine, St. Louis, MO

Robert Leviton MD, MPH, FACEP, ABPM-CI, FAMIA

• Assistant Professor
• New York Medical College
• Department of Emergency Medicine
• Valhalla, NY

Karen A. Monsen PhD, RN, FAMIA, FAAN

• Professor, School of Nursing
• University of Minnesota, Minneapolis, MN

Robert Muenchen MS, PSTAT

• Manager, Research Computing Support
• University of Tennessee, Knoxville, TN

Dallas Snider PhD

• Chair, Department of Information Technology
• University of West Florida, Pensacola, FL

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2. Table of Contents

OVERVIEW OF BIOMEDICAL DATA SCIENCE

1. Introduction
2. Background and history
3. Conflicting perspectives
   1. the statistician’s perspective
   2. the machine learner’s perspective
   3. the database administrator’s perspective
   4. the data visualizer’s perspective
4. Data analytical processes
   1. raw data
   2. data pre-processing
   3. exploratory data analysis (EDA)
   4. predictive modeling approaches
   5. types of models
   6. types of software
5. Major types of analytics
   1. descriptive analytics
   2. diagnostic analytics
   3. predictive analytics (modeling)
   4. prescriptive analytics
6. Putting it all together
7. Biomedical data science tools
8. Biomedical data science education
9. Biomedical data science careers
10. Importance of soft skills in data science
11. Biomedical data science resources
12. Biomedical data science challenges
13. Future trends
14. Conclusion
15. References

SPREADSHEET TOOLS AND TIPS

1. Introduction
   1. basic spreadsheet functions
   2. download the sample spreadsheet
2. Navigating the worksheet
3. Clinical application of spreadsheets
   1. formulas and functions
   2. filter
   3. sorting data
   4. freezing panes
   5. conditional formatting
   6. pivot tables
   7. visualization
   8. data analysis
4. Tips and tricks
   1. Microsoft Excel shortcuts – windows users
   2. Google sheets tips and tricks
5. Conclusions
6. Exercises
7. References

BIOSTATISTICS PRIMER

1. Introduction
2. Measures of central tendency & dispersion
   1. the normal and log-normal distributions
3. Descriptive and inferential statistics
4. Categorical data analysis
5. Diagnostic tests
6. Bayes’ theorem
7. Types of research studies
   1. observational studies
   2. interventional studies
   3. meta-analysis
8. Correlation
9. Linear regression
10. Comparing two groups
    1. the independent-samples t-test
    2. the wilcoxon-mann-whitney test
11. Comparing more than two groups
12. Other types of tests
    1. generalized tests
    2. exact or permutation tests
    3. bootstrap or resampling tests
13. Stats packages and online calculators
    1. commercial packages
    2. non-commercial or open source packages
    3. online calculators
14. Challenges
15. Future trends
16. Conclusion
17. Exercises
18. References

DATA VISUALIZATION

1. Introduction
   1. historical data visualizations
   2. visualization frameworks
2. Visualization basics
3. Data visualization software
   1. Microsoft Excel
   2. Google sheets
   3. Tableau
   4. R programming language
   5. other visualization programs
4. Visualization options
   1. visualizing categorical data
   2. visualizing continuous data
5. Dashboards
6. Geographic maps
7. Challenges
8. Conclusion
9. Exercises
10. References

INTRODUCTION TO DATABASES

1. Introduction
2. Definitions
3. A brief history of database models
   1. hierarchical model
   2. network model
   3. relational model
4. Relational database structure
5. Clinical data warehouses (CDWs)
6. Structured query language (SQL)
7. Learning SQL
8. Conclusion
9. Exercises
10. References

BIG DATA

1. Introduction
2. The seven v’s of big data related to health care data
3. Technical background
4. Application
5. Challenges
   1. technical
   2. organizational
   3. legal
   4. translational
6. Future trends
7. Conclusion
8. References

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BIOINFORMATICS and PRECISION MEDICINE

1. Introduction
2. History
3. Definitions
4. Biological data analysis – from data to discovery
5. Biological data types
   1. genomics
   2. transcriptomics
   3. proteomics
   4. bioinformatics data in public repositories
   5. biomedical cancer data portals
6. Tools for analyzing bioinformatics data
   1. command line tools
   2. web-based tools
7. Genomic data analysis
8. Genomic data analysis workflow
   1. variant calling pipeline for whole exome sequencing data
   2. quality check
   3. alignment
   4. variant calling
   5. variant filtering and annotation
   6. downstream analysis
   7. reporting and visualization
9. Precision medicine – from big data to patient care
10. Examples of precision medicine
11. Challenges
12. Future trends
13. Useful resources
14. Conclusion
15. Exercises
16. References

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PROGRAMMING LANGUAGES FOR DATA ANALYSIS

1. Introduction
2. History
3. R language
   1. installing R & rstudio
   2. an example R program
   3. getting help in R
   4. user interfaces for R
   5. R’s default user interface: rgui
   6. Rstudio
   7. menu & dialog guis
   8. some popular R guis
   9. R graphical user interface comparison
   10. R resources
4. Python language
   1. installing Python
   2. an example Python program
   3. getting help in Python
   4. user interfaces for Python
   5. reproducibility
5. R vs. Python
6. Future trends
7. Conclusion
8. Exercises
9. References

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MACHINE LEARNING

1. Brief history
2. Introduction
   1. data refresher
   2. training vs test data
   3. bias and variance
   4. supervised and unsupervised learning
3. Common machine learning algorithms
4. Supervised learning
5. Unsupervised learning
   1. dimensionality reduction
   2. reinforcement learning
   3. semi-supervised learning
6. Evaluation of predictive analytical performance
   1. classification model evaluation
   2. regression model evaluation
7. Machine learning software
   1. Weka
   2. Orange
   3. Rapidminer studio
   4. Knime
   5. Google tensorflow 2
   6. honorable mention
   7. summary
8. Programming languages and machine learning
9. Machine learning challenges
10. Machine learning examples
    1. example 1 classification
    2. example 2 regression
    3. example 3 clustering
    4. example 4 association rules
11. Conclusion
12. Exercises
13. References

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ARTIFICIAL INTELLIGENCE

1. Introduction
   1. definitions
2. History
3. Ai architectures
4. Deep learning
5. Image analysis (computer vision)
   1. Radiology
   2. Ophthalmology
   3. Dermatology
   4. Pathology
   5. Cardiology
   6. Neurology
   7. Wearable devices
   8. image libraries and packages
6. Natural language processing
   1. NLP libraries and packages
   2. text mining and medicine
   3. speech recognition
7. Electronic health record data and AI
8. Genomic analysis
9. AI platforms
   1. deep learning platforms and programs
10. Artificial intelligence challenges
    1. general
    2. data issues
    3. technical
    4. socio economic and legal
    5. regulatory
    6. adverse unintended consequences
    7. need for more ML and AI education
11. Future trends
12. Conclusion
13. Exercises
14. References

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BIOMEDICAL DATA SCIENCE RESOURCES