Abstract
Electronic medical record (EMR) data contains historical sequences of visits of patients, and each visit contains rich information, such as patient demographics, hospital utilisation and medical codes, including diagnosis, procedure and medication codes. Most existing EMR embedding methods capture visit-code associations by constructing input visit representations as binary vectors with a static vocabulary of medical codes. With this limited representation, they fail in encapsulating rich attribute information of visits (demographics and utilisation information) and/or codes (e.g., medical code descriptions). Furthermore, current work considers visits of the same patient as discrete-time events and ignores time gaps between them. However, the time gaps between visits depict dynamics of the patient's medical history inducing varying influences on future visits. To address these limitations, we present MedGraph, a supervised EMR embedding method that captures two types of information: (1) the visit-code associations in an attributed bipartite graph, and (2) the temporal sequencing of visits through a point process. MedGraph produces Gaussian embeddings for visits and codes to model the uncertainty. We evaluate the performance of MedGraph through an extensive experimental study and show that MedGraph outperforms state-of-the-art EMR embedding methods in several medical risk prediction tasks.
Original language | English |
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Title of host publication | ECAI Digital - 2020 |
Subtitle of host publication | 24th European Conference on Artificial Intelligence |
Editors | Giuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senén Barro, Alberto Bugarín, Jérôme Lang |
Place of Publication | Amsterdam Netherlands |
Publisher | IOS Press |
Pages | 1810-1817 |
Number of pages | 8 |
ISBN (Electronic) | 9781643681016 |
ISBN (Print) | 9781643681009 |
DOIs | |
Publication status | Published - 24 Aug 2020 |
Event | European Conference on Artificial Intelligence 2020 - Virtual, Santiago de Compostela, Spain Duration: 29 Aug 2020 → 8 Sept 2020 Conference number: 24th https://digital.ecai2020.eu (Website) http://ebooks.iospress.nl/volume/ecai-2020-24th-european-conference-on-artificial-intelligence (Proceedings) |
Publication series
Name | Frontiers in Artificial Intelligence and Applications |
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Volume | 325 |
ISSN (Print) | 0922-6389 |
Conference
Conference | European Conference on Artificial Intelligence 2020 |
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Abbreviated title | ECAI 2020 |
Country/Territory | Spain |
City | Santiago de Compostela |
Period | 29/08/20 → 8/09/20 |
Other | 24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 |
Internet address |