This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.
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AI in Healthcare SpecializationStanford UniversityAbout this Course
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Course 2 of 5 in the
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Beginner Level
Approx. 12 hours to complete
English
Subtitles: English
What you will learn
How to apply a framework for medical data mining
Ethical use of data in healthcare decisions
How to make use of data that may be inaccurate in systematic ways
What makes a good research question and how to construct a data mining workflow answer it
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 12 hours to complete
English
Subtitles: English
Offered by
Stanford University
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Syllabus - What you will learn from this course
1 hour to complete
Asking and answering questions via clinical data mining
1 hour to complete
12 videos (Total 19 min), 2 readings, 3 quizzes
12 videos
Welcome2m
Introduction to the data mining workflow1m
Real Life Example1m
Example: Finding similar patients2m
Example: Estimating risk37s
Putting patient data on timeline51s
Revisit the data mining workflow steps1m
Types of research questions2m
Research questions suited for clinical data55s
Example: making decision to treat1m
Properties that make answering a research question useful1m
Wrap Up1m
2 readings
Study Guide Module 15m
Citations and Additional Readings5m
3 practice exercises
Reflection Exercise10m
Reflection Exercise10m
Knowledge Check30m
2 hours to complete
Data available from Healthcare systems
2 hours to complete
16 videos (Total 32 min), 3 readings, 4 quizzes
16 videos
Review of key entities and the data they collect1m
Actors with different interests2m
Common data types in Healthcare2m
Strengths and weaknesses of observational data2m
Bias and error from the healthcare system perspective2m
Bias and error of exposures and outcomes50s
How a patient's exposure might be misclassified2m
How a patient's outcome could be misclassified2m
Electronic medical record data2m
Claims data2m
Pharmacy55s
Surveillance datasets and Registries1m
Population health data sets3m
A framework to assess if a data source is useful1m
Wrap Up1m
3 readings
Video Image Credit
Study Guide Module 25m
Citations and Additional Readings5m
4 practice exercises
Reflection Exercise10m
Reflection Exercise10m
Reflection Exercise15m
Knowledge Check30m
1 hour to complete
Representing time, and timing of events, for clinical data mining
1 hour to complete
12 videos (Total 20 min), 2 readings, 3 quizzes
12 videos
Introduction37s
Time, timelines, timescales and representations of time2m
Timescale: Choosing the relevant units of time29s
What affects the timescale1m
Representation of time57s
Time series and non-time series data2m
Order of events1m
Implicit representations of time1m
Different ways to put data in bins2m
Timing of exposures and outcomes3m
Clinical processes are non-stationary2m
Wrap Up1m
2 readings
Study Guide Module 35m
Citations and Additional Readings5m
3 practice exercises
Reflection Exercise10m
Reflection Exercise 215m
Knowledge Check30m
2 hours to complete
Creating analysis ready datasets from patient timelines
2 hours to complete
18 videos (Total 33 min), 2 readings, 3 quizzes
18 videos
Defining the unit of analysis52s
Using features and the presence of features2m
How to create features from structured sources1m
Standardizing features52s
Dealing with too many features3m
The origins of missing values3m
Dealing with missing values1m
Summary recommendations for missing values1m
Constructing new features1m
Examples of engineered features2m
When to consider engineered features1m
Main points about creating analysis ready datasets53s
Structured knowledge graphs2m
So what exactly is in a knowledge graph2m
What are important knowledge graphs2m
How to choose which knowledge graph to use1m
Wrap Up45s
2 readings
Study Guide Module 45m
Citations and Additional Readings5m
3 practice exercises
Reflection Exercise10m
Reflection Exercise20m
Knowledge Check30m
Reviews
TOP REVIEWS FROM INTRODUCTION TO CLINICAL DATA
by YXNov 4, 2020
Very clear and well-organized course. I have learned quite a bit about the different types of clinical data, why they are important, and how to transfer them to analytical useable data sets.
by SROct 26, 2020
Good introductory course covering key features of the various data that are gathered or used in health care. No prior knowledge about the topic is required.
by NJNov 19, 2020
It would be helpful to be able to see all items that need to be completed. I took the final test and it says I've only completed 2 of 5 courses.
by KSOct 16, 2020
Nicely Framed and Executed in a simple language so anyone can catch up earliest.
About the AI in Healthcare Specialization
Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.
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