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dc.contributor.authorIm, SJ
dc.contributor.authorXu, Y
dc.contributor.authorWatson, J
dc.contributor.authorBonner, A
dc.contributor.authorHealy, H
dc.contributor.authorHoy, W
dc.date.accessioned2021-02-12T02:54:47Z
dc.date.available2021-02-12T02:54:47Z
dc.date.issued2020
dc.identifier.isbn9781728125473en_US
dc.identifier.doi10.1109/SSCI47803.2020.9308381en_US
dc.identifier.urihttp://hdl.handle.net/10072/402053
dc.description.abstractAvoidable hospital readmission is problematic as it increases the burden on healthcare systems, leads to a shortage of hospital beds and impacts on the costs of healthcare. Various machine learning algorithms have been applied to predict patient readmissions. However, existing literature has only focused on individual features of health conditions without consideration of associations between features. This paper proposes discriminative pattern-based features as a technique to improve readmission prediction. First, discriminative patterns that occur disproportionately between two classes: readmission and non-readmission, were generated based on hospital electronic health records. Second, the patterns were fed as features into a classification model for readmission prediction. Then, we have evaluated these discriminative pattern-based features in three datasets: diabetes, chronic kidney disease and all diseases. Experiments with each dataset showed that the features of chronic disease cohorts have fewer differences between the readmission and the non-readmission classes than the all-diseases cohort. Our proposed pattern-based model improved the prediction performance in terms of AUC (Area Under the receiver operating characteristic curve) by about 12% compared with the baseline models for the all-disease cohort, however, it showed little improvement for either diabetes or chronic kidney disease datasets.en_US
dc.description.peerreviewedYesen_US
dc.publisherIEEEen_US
dc.relation.ispartofconferencename2020 IEEE Symposium Series on Computational Intelligence (SSCI)en_US
dc.relation.ispartofconferencetitle2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020en_US
dc.relation.ispartofdatefrom2020-12-01
dc.relation.ispartofdateto2020-12-04
dc.relation.ispartoflocationCanberra, Australiaen_US
dc.relation.ispartofpagefrom50en_US
dc.relation.ispartofpageto57en_US
dc.subject.fieldofresearchPublic Health and Health Servicesen_US
dc.subject.fieldofresearchClinical Sciencesen_US
dc.subject.fieldofresearchcode1117en_US
dc.subject.fieldofresearchcode1103en_US
dc.titleHospital Readmission Prediction using Discriminative patternsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conferencesen_US
dcterms.bibliographicCitationIm, SJ; Xu, Y; Watson, J; Bonner, A; Healy, H; Hoy, W, Hospital Readmission Prediction using Discriminative patterns, 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, pp. 50-57en_US
dc.date.updated2021-02-12T01:33:07Z
dc.description.versionAccepted Manuscript (AM)en_US
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.en_US
gro.hasfulltextFull Text
gro.griffith.authorBonner, Ann J.


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