Data Foundations for Machine Learning in Medicine

Monday, August 12, 2024, 12:00 AM - Wednesday, August 11, 2027, 11:59 PM, On Demand

This course is focused on data's pivotal role in healthcare machine learning. It thoroughly addresses the structural and analytical aspects of various data types within the medical domain. The course content is designed to provide a comprehensive understanding of working with healthcare databases, knowledge graphs, structured and unstructured data, and techniques for electronic phenotyping and time series analysis. The course's main emphasis is on deepening your understanding of data management and processing challenges and developing the practical data skills required for applying machine learning to complex healthcare datasets.

  Release Date: August 12, 2024
  Expiration Date: August 11, 2027
  Estimated Time to Complete: 10.0 hours
  Registration Fee:  $675 per course; $1299 All-Access Plan

Click Begin (at the top) to learn more about how to enroll in the course.

View entire series: This is the first course in our Applications of Machine Learning in Medicine program. View the second course in the program: Model Development, Deployment, and Ethical Considerations. 

AMA PRA Category 1 Credits™ (10.00 hours), Non-Physician Participation Credit (10.00 hours)

1. Discuss the structural and analytical aspects of various data types specific to the medical field, including healthcare databases and knowledge graphs.
2. Develop skills in handling the complexities of data management within healthcare, such as dealing with structured and unstructured data, electronic phenotyping, and time series analysis.
3. Acquire the practical skills necessary to clean, process, and prepare healthcare data for effective machine learning applications.
4. Navigate and extract meaningful information from EHRs to support machine learning models.
5. Describe the methodologies for identifying and categorizing patient subgroups based on electronic health records, aiding in personalized treatment and 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. 

Credit Designation 
American Medical Association (AMA) 
Stanford Medicine designates this Enduring Material for a maximum of 10.00 AMA PRA Category 1 Credits^TM.  Physicians should claim only the credit commensurate with the extent of their participation in the activity. 

Accessibility Statement
 Stanford University School of Medicine is committed to ensuring that its programs, services, goods and facilities are accessible to individuals with disabilities as specified under Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Amendments Act of 2008.  If you have needs that require special accommodations, please contact the CME.

Cultural and Linguistic Competency
The planners and speakers of this CME activity have been encouraged to address cultural issues relevant to their topic area for the purpose of complying with California Assembly Bill 1195. Moreover, the Stanford University School of Medicine Multicultural Health Portal contains many useful cultural and linguistic competency tools including culture guides, language access information and pertinent state and federal laws.  You are encouraged to visit the Multicultural Health Portal: https://laneguides.stanford.edu/multicultural-health

Reference/Bibliography List
For additional resources, please visit the course.

For activity related questions, please contact
  Ph: 650.204.3984
  Email: [email protected]

For CME general questions, please contact 
   Email: [email protected]

Mitigation of Relevant Financial Relationships

Stanford Medicine adheres to the Standards for Integrity and Independence in Accredited Continuing Education.

The content of this activity is not related to products or the business lines of an ACCME-defined ineligible company. Hence, there are no relevant financial relationships with an ACCME-defined ineligible company for anyone who was in control of the content of this activity.