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Readmission prediction using deep learning on electronic health records
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Halland Hospital, Region Halland, Sweden.ORCID-id: 0000-0001-5688-0156
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-3495-2961
Halland Hospital, Region Halland, Sweden & Institute of Medicine, Dept. of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
2019 (Engelska)Ingår i: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 97, artikel-id 103256Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7,500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We show that the model with all key elements achieves a higher discrimination ability (AUC 0.77) compared to the rest. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients. © 2019 The Authors

Ort, förlag, år, upplaga, sidor
Maryland Heights, MO: Academic Press, 2019. Vol. 97, artikel-id 103256
Nyckelord [en]
Electronic health records, Readmission prediction, Long short-term memory networks, Contextual embeddings
Nationell ämneskategori
Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi
Identifikatorer
URN: urn:nbn:se:hh:diva-39297DOI: 10.1016/j.jbi.2019.103256PubMedID: 31351136Scopus ID: 2-s2.0-85069858722OAI: oai:DiVA.org:hh-39297DiVA, id: diva2:1308084
Projekt
HiCube - behovsmotiverad hälsoinnovation
Forskningsfinansiär
Europeiska regionala utvecklingsfonden (ERUF)
Anmärkning

Funding: The authors thank the European Regional Development Fund (ERDF), Health Technology Center and CAISR at Halmstad University and Hallands Hospital for financing the research work under the project HiCube - behovsmotiverad hälsoinnovation.

Tillgänglig från: 2019-04-30 Skapad: 2019-04-30 Senast uppdaterad: 2019-09-10Bibliografiskt granskad
Ingår i avhandling
1. Predicting clinical outcomes via machine learning on electronic health records
Öppna denna publikation i ny flik eller fönster >>Predicting clinical outcomes via machine learning on electronic health records
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

The rising complexity in healthcare, exacerbated by an ageing population, results in ineffective decision-making leading to detrimental effects on care quality and escalates care costs. Consequently, there is a need for smart decision support systems that can empower clinician's to make better informed care decisions. Decisions, which are not only based on general clinical knowledge and personal experience, but also rest on personalised and precise insights about future patient outcomes. A promising approach is to leverage the ongoing digitization of healthcare that generates unprecedented amounts of clinical data stored in Electronic Health Records (EHRs) and couple it with modern Machine Learning (ML) toolset for clinical decision support, and simultaneously, expand the evidence base of medicine. As promising as it sounds, assimilating complete clinical data that provides a rich perspective of the patient's health state comes with a multitude of data-science challenges that impede efficient learning of ML models. This thesis primarily focuses on learning comprehensive patient representations from EHRs. The key challenges of heterogeneity and temporality in EHR data are addressed using human-derived features appended to contextual embeddings of clinical concepts and Long-Short-Term-Memory networks, respectively. The developed models are empirically evaluated in the context of predicting adverse clinical outcomes such as mortality or hospital readmissions. We also present evidence that, surprisingly, different ML models primarily designed for non-EHR analysis (like language processing and time-series prediction) can be combined and adapted into a single framework to efficiently represent EHR data and predict patient outcomes.

Ort, förlag, år, upplaga, sidor
Halmstad: Halmstad University Press, 2019
Serie
Halmstad University Dissertations ; 58
Nationell ämneskategori
Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi
Identifikatorer
urn:nbn:se:hh:diva-39309 (URN)978-91-88749-24-6 (ISBN)978-91-88749-25-3 (ISBN)
Presentation
2019-05-23, R4318, R Building, Halmstad University, Halmstad, Sweden, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-05-06 Skapad: 2019-05-02 Senast uppdaterad: 2019-05-06Bibliografiskt granskad

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Ashfaq, AwaisPinheiro Sant'Anna, AnitaNowaczyk, Sławomir

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Ashfaq, AwaisPinheiro Sant'Anna, AnitaNowaczyk, Sławomir
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Journal of Biomedical Informatics
Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi

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