Please use this identifier to cite or link to this item: http://hdl.handle.net/11054/721
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dc.contributor.authorWang, Jim*
dc.contributor.authorLim, Chee Peng*
dc.contributor.authorCreighton, Douglas*
dc.contributor.authorKhorsavi, Abbas*
dc.contributor.authorNahavandi, Saeid*
dc.contributor.authorUgon, Julien*
dc.contributor.authorVamplew, Peter*
dc.contributor.authorStranieri, Andrew*
dc.contributor.authorMartin, Laura*
dc.contributor.authorFreischmidt, Anton*
dc.date.accessioned2015-06-25T04:30:21Zen
dc.date.available2015-06-25T04:30:21Zen
dc.date.issued2015en
dc.identifier.govdoc00706en
dc.identifier.issn1433-3058en
dc.identifier.urihttp://hdl.handle.net/11054/721en
dc.description.abstractA useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.en
dc.description.provenanceSubmitted by Gemma Siemensma (gemmas@bhs.org.au) on 2015-06-23T04:44:43ZNo. of bitstreams: 0en
dc.description.provenanceApproved for entry into archive by Gemma Siemensma (gemmas@bhs.org.au) on 2015-06-25T04:30:21Z (GMT) No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2015-06-25T04:30:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2015en
dc.publisherSpringer Verlagen
dc.titlePatient admission prediction using a pruned fuzzy min-max neural network with rule extraction.en
dc.typeJournal Articleen
dc.type.specifiedArticleen
dc.bibliographicCitation.titleNeural Computing and Applicationsen
dc.bibliographicCitation.volume26en
dc.bibliographicCitation.stpage277en
dc.bibliographicCitation.endpage289en
dc.publisher.placeGermanyen
dc.subject.healththesaurusPATIENT ADMISSION PREDICTIONen
dc.subject.healththesaurusFUZZY MIN-MAX NEURAL NETWORKen
dc.subject.healththesaurusGENETIC ALGORITHMen
dc.subject.healththesaurusRULE EXTRACTIONen
dc.subject.healththesaurusEMERGENCY DEPARTMENTen
dc.subject.healththesaurusPATIENT ADMISSIONen
dc.subject.healththesaurusEMERGENCY SERVICE, HOSPITALen
dc.subject.healththesaurusEMERGENCY DEPARTMENT MANAGEMENTen
dc.date.issuedbrowse2015-01-01en
Appears in Collections:Research Output

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