Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
© 2019 The Authors. Increasingly large electronic health records (EHRs) provide an opportunity to algorithmi-cally learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refi...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
World Scientific Pub Co Pte Lt
2021
|
| Online Access: | https://hdl.handle.net/1721.1/137717 |
| _version_ | 1826203198627512320 |
|---|---|
| author | Chen, Irene Y Agrawal, Monica Horng, Steven Sontag, David |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Chen, Irene Y Agrawal, Monica Horng, Steven Sontag, David |
| author_sort | Chen, Irene Y |
| collection | MIT |
| description | © 2019 The Authors. Increasingly large electronic health records (EHRs) provide an opportunity to algorithmi-cally learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for which patients the graph is most accurate. We identify sample size and unmeasured confounders as major sources of error in the health knowledge graph. We introduce a method to leverage non-linear functions in building the causal graph to better understand existing model assumptions. Finally, to assess model generalizability, we extend to a larger set of complete patient visits within a hospital system. We conclude with a discussion on how to robustly extract medical knowl-edge from EHRs. Supplementary material: http://clinicalml.org/papers/ChenEtAl PSB20 suppl.pdf. |
| first_indexed | 2024-09-23T12:32:52Z |
| format | Article |
| id | mit-1721.1/137717 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:32:52Z |
| publishDate | 2021 |
| publisher | World Scientific Pub Co Pte Lt |
| record_format | dspace |
| spelling | mit-1721.1/1377172023-04-10T13:29:08Z Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph Chen, Irene Y Agrawal, Monica Horng, Steven Sontag, David Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science © 2019 The Authors. Increasingly large electronic health records (EHRs) provide an opportunity to algorithmi-cally learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for which patients the graph is most accurate. We identify sample size and unmeasured confounders as major sources of error in the health knowledge graph. We introduce a method to leverage non-linear functions in building the causal graph to better understand existing model assumptions. Finally, to assess model generalizability, we extend to a larger set of complete patient visits within a hospital system. We conclude with a discussion on how to robustly extract medical knowl-edge from EHRs. Supplementary material: http://clinicalml.org/papers/ChenEtAl PSB20 suppl.pdf. 2021-11-08T17:13:57Z 2021-11-08T17:13:57Z 2020-01 2021-01-26T18:44:37Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137717 Chen, Irene Y, Agrawal, Monica, Horng, Steven and Sontag, David. 2020. "Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph." Pacific Symposium on Biocomputing, 25 (2020). en 10.1142/9789811215636_0003 Pacific Symposium on Biocomputing Creative Commons Attribution NonCommercial License 4.0 https://creativecommons.org/licenses/by-nc/4.0/ application/pdf World Scientific Pub Co Pte Lt World Scientific |
| spellingShingle | Chen, Irene Y Agrawal, Monica Horng, Steven Sontag, David Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph |
| title | Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph |
| title_full | Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph |
| title_fullStr | Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph |
| title_full_unstemmed | Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph |
| title_short | Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph |
| title_sort | robustly extracting medical knowledge from ehrs a case study of learning a health knowledge graph |
| url | https://hdl.handle.net/1721.1/137717 |
| work_keys_str_mv | AT chenireney robustlyextractingmedicalknowledgefromehrsacasestudyoflearningahealthknowledgegraph AT agrawalmonica robustlyextractingmedicalknowledgefromehrsacasestudyoflearningahealthknowledgegraph AT horngsteven robustlyextractingmedicalknowledgefromehrsacasestudyoflearningahealthknowledgegraph AT sontagdavid robustlyextractingmedicalknowledgefromehrsacasestudyoflearningahealthknowledgegraph |