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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.date.accessioned2021-04-29T03:54:28Z
dc.date.available2021-04-29T03:54:28Z
dc.date.issued2021
dc.identifier.isbn9783030676636en_US
dc.identifier.issn0302-9743en_US
dc.identifier.doi10.1007/978-3-030-67664-3_43en_US
dc.identifier.urihttp://hdl.handle.net/10072/404023
dc.description.abstractUnderstanding patients’ journeys in healthcare system is a fundamental prepositive task for a broad range of AI-based healthcare applications. This task aims to learn an informative representation that can comprehensively encode hidden dependencies among medical events and its inner entities, and then the use of encoding outputs can greatly benefit the downstream application-driven tasks. A patient journey is a sequence of electronic health records (EHRs) over time that is organized at multiple levels: patient, visits and medical codes. The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes. This paper proposes a novel self-attention mechanism that can simultaneously capture the contextual and temporal relationships hidden in patient journeys. A multi-level self-attention network (MusaNet) is specifically designed to learn the representations of patient journeys that is used to be a long sequence of activities. We evaluated the efficacy of our method on two medical application tasks with real-world benchmark datasets. The results have demonstrated the proposed MusaNet produces higher-quality representations than state-of-the-art baseline methods. The source code is available in https://github.com/xueping/MusaNet.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherSpringeren_US
dc.publisher.placeSwitzerlanden_US
dc.relation.ispartofconferencenameECML PKDD 2020 Joint European Conference on Machine Learning and Knowledge Discovery in Databasesen_US
dc.relation.ispartofconferencetitleLecture Notes in Computer Scienceen_US
dc.relation.ispartofdatefrom2020-09-14
dc.relation.ispartofdateto2020-09-18
dc.relation.ispartoflocationGhent, Belgiumen_US
dc.relation.ispartofpagefrom719en_US
dc.relation.ispartofpageto735en_US
dc.relation.ispartofvolume12459en_US
dc.subject.fieldofresearchPublic Health and Health Servicesen_US
dc.subject.fieldofresearchInformation Systemsen_US
dc.subject.fieldofresearchcode1117en_US
dc.subject.fieldofresearchcode0806en_US
dc.titleSelf-attention Enhanced Patient Journey Understanding in Healthcare Systemen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conferencesen_US
dcterms.bibliographicCitationPeng, X; Long, G; Shen, T; Wang, S; Jiang, J, Self-attention Enhanced Patient Journey Understanding in Healthcare System, Lecture Notes in Computer Science, 2021, 12459, pp. 719-735en_US
dc.date.updated2021-04-27T22:35:11Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.rights.copyright© Springer Nature Switzerland AG 2021. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_US
gro.hasfulltextFull Text
gro.griffith.authorWang, Sen


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