Stanford University · Course
Data Foundations for Machine Learning in Medicine (XMLPH110)
Data Foundations for Machine Learning in Medicine (XMLPH110) is a self-paced Stanford Medicine online course on data in healthcare machine learning: healthcare databases, knowledge graphs, structured and unstructured data, electronic phenotyping and time series analysis.
Who it suits
- Clinicians and analysts who want practical data skills for healthcare machine learning.
- People working toward the Applications of Machine Learning in Medicine Program.
What it covers
- The structure of the US healthcare system and its stakeholders.
- Hospital, insurance and population health databases.
- Knowledge graphs, and structured and unstructured data.
- Electronic phenotyping and time series analysis.
Common questions about Data Foundations for Machine Learning in Medicine (XMLPH110)
- How long is it?
- Stanford lists 10 hours, 100% online and on demand, with a Record of Completion.
- Is there CME credit?
- Stanford lists 10 CME credits; check its page for the terms.
- Which programme is it in?
- The Applications of Machine Learning in Medicine Program.
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Where this comes from
Written from Stanford University’s own pages, read on 2026-10-14. Content, prices and availability change, so the provider’s page is the source. We are not affiliated with Stanford University and this is not an endorsement.