Using foundation models to learn how to represent electronic health records
Abstract/Contents
- Abstract
- The invention and use of electronic health records has enabled new applications of machine learning in the field of healthcare. One particularly important application is the use of machine learning to predict the risk of a variety of clinical outcomes given electronic health records. However, training machine learning models on electronic health records is often challenging due to limited data set sizes and the intrinsic complexity of electronic health record data. In this dissertation, I propose and validate the use of foundation models as a way to learn representations for electronic health records, taking advantage of the longitudinal structure of medical record data to learn transferable representations. These representations can then be used to improve our ability to predict patient risk, achieving superior ranking performance, increased robustness across both time and space, and better label efficiency.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2024; ©2024 |
| Publication date | 2024; 2024 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Steinberg, Ethan Hannan |
|---|---|
| Degree supervisor | Leskovec, Jurij |
| Degree supervisor | Shah, Nigam |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Shah, Nigam |
| Thesis advisor | Ré, Christopher |
| Degree committee member | Ré, Christopher |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ethan Steinberg. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2024. |
| Location | https://purl.stanford.edu/tx757rn7194 |
Access conditions
- Copyright
- © 2024 by Ethan Hannan Steinberg
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