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.
In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.
Overview
Syllabus
- Asking and answering questions via clinical data mining
- Data available from Healthcare systems
- Representing time, and timing of events, for clinical data mining
- Creating analysis ready datasets from patient timelines
- Handling unstructured healthcare data: text, images, signals
- Putting the pieces together: Electronic phenotyping
- Ethics
- Course Conclusion
Taught by
Nigam Shah, Steven Bagley and David Magnus
