MedGraph: structural and temporal representation learning of electronic medical records

Bhagya Hettige, Weiqing Wang, Yuan-Fang Li, Suong Le, Wray Buntine

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationECAI Digital - 2020
Subtitle of host publication24th European Conference on Artificial Intelligence
EditorsGiuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senén Barro, Alberto Bugarín, Jérôme Lang
Place of PublicationAmsterdam Netherlands
PublisherIOS Press
Pages1810-1817
Number of pages8
ISBN (Electronic)9781643681016
ISBN (Print)9781643681009
DOIs
Publication statusPublished - 24 Aug 2020
EventEuropean Conference on Artificial Intelligence 2020 - Virtual, Santiago de Compostela, Spain
Duration: 29 Aug 20208 Sep 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

NameFrontiers in Artificial Intelligence and Applications
Volume325
ISSN (Print)0922-6389

Conference

ConferenceEuropean Conference on Artificial Intelligence 2020
Abbreviated titleECAI 2020
Country/TerritorySpain
CitySantiago de Compostela
Period29/08/208/09/20
Other24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020
Internet address

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