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dc.contributor.authorPeng, X
dc.contributor.authorLong, G
dc.contributor.authorShen, T
dc.contributor.authorWang, S
dc.contributor.authorJiang, J
dc.contributor.authorZhang, C
dc.date.accessioned2021-03-07T23:46:53Z
dc.date.available2021-03-07T23:46:53Z
dc.date.issued2020
dc.identifier.isbn9781728183169
dc.identifier.issn1550-4786
dc.identifier.doi10.1109/ICDM50108.2020.00050
dc.identifier.urihttp://hdl.handle.net/10072/402895
dc.description.abstractElectronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems. A patient's EHR data is organized as a three-level hierarchy from top to bottom: patient journey - all the experiences of diagnoses and treatments over a period of time; individual visit - a set of medical codes in a particular visit; and medical code - a specific record in the form of medical codes. As EHRs begin to amass in millions, the potential benefits, which these data might hold for medical research and medical outcome prediction, are staggering - including, for example, predicting future admissions to hospitals, diagnosing illnesses or determining the efficacy of medical treatments. Each of these analytics tasks requires a domain knowledge extraction method to transform the hierarchical patient journey into a vector representation for further prediction procedure. The representations should embed a sequence of visits and a set of medical codes with a specific timestamp, which are crucial to any downstream prediction tasks. Hence, expressively powerful representations are appealing to boost learning performance. To this end, we propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys, based solely on the proposed attention mechanism. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset. The empirical results demonstrate the proposed BiteNet model produces higher-quality representations than state-of-the-art baseline methods.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2020 IEEE International Conference on Data Mining (ICDM)
dc.relation.ispartofconferencetitleProceedings - IEEE International Conference on Data Mining, ICDM
dc.relation.ispartofdatefrom2020-11-17
dc.relation.ispartofdateto2020-11-20
dc.relation.ispartofpagefrom412
dc.relation.ispartofpageto421
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode4609
dc.titleBiteNet: Bidirectional temporal encoder network to predict medical outcomes
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationPeng, X; Long, G; Shen, T; Wang, S; Jiang, J; Zhang, C, BiteNet: Bidirectional temporal encoder network to predict medical outcomes, Proceedings - IEEE International Conference on Data Mining, ICDM, 2020, pp. 412-421
dc.date.updated2021-03-05T04:59:07Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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gro.griffith.authorWang, Sen


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