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Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

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dc.contributor.authorLu, Changen
dc.contributor.authorReddy, Chandan K.en
dc.contributor.authorNing, Yueen
dc.date.accessioned2022-01-19T22:07:55Zen
dc.date.available2022-01-19T22:07:55Zen
dc.date.issued2021-09-20en
dc.date.updated2022-01-19T22:07:53Zen
dc.description.abstractElectronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed model's strengths in both predictive tasks and interpretable abilities.en
dc.description.versionAccepted versionen
dc.format.extent13 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TCYB.2021.3109881en
dc.identifier.eissn2168-2275en
dc.identifier.issn2168-2267en
dc.identifier.issue99en
dc.identifier.orcidReddy, Chandan [0000-0003-2839-3662]en
dc.identifier.pmid34546938en
dc.identifier.urihttp://hdl.handle.net/10919/107797en
dc.identifier.volumePPen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000733513800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectAutomation & Control Systemsen
dc.subjectComputer Science, Artificial Intelligenceen
dc.subjectComputer Science, Cyberneticsen
dc.subjectComputer Scienceen
dc.subjectDiseasesen
dc.subjectMedical diagnostic imagingen
dc.subjectTask analysisen
dc.subjectCodesen
dc.subjectPredictive modelsen
dc.subjectTrainingen
dc.subjectMedical servicesen
dc.subjectElectronic health records (EHRs)en
dc.subjectevent predictionen
dc.subjectgraph learningen
dc.subjecthyperbolic embeddingsen
dc.subjectmodel interpretabilityen
dc.subject0102 Applied Mathematicsen
dc.subject0801 Artificial Intelligence and Image Processingen
dc.subject0906 Electrical and Electronic Engineeringen
dc.subjectArtificial Intelligence & Image Processingen
dc.titleSelf-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Predictionen
dc.title.serialIEEE Transactions on Cyberneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherEarly Accessen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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