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Retrospective identification of latent subgroups of emergency department patients: A machine learning approach.

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Title: Retrospective identification of latent subgroups of emergency department patients: A machine learning approach.
Authors: Duwalage KI; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.; Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Melbourne, Victoria, Australia., Burkett E; Emergency Department, Princess Alexandra Hospital, Brisbane, Queensland, Australia.; Queensland Government Clinical Excellence Division, Healthcare Improvement Unit, Brisbane, Queensland, Australia., White G; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.; Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Melbourne, Victoria, Australia., Wong A; Emergency Department, Princess Alexandra Hospital, Brisbane, Queensland, Australia., Thompson MH; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.; Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
Source: Emergency medicine Australasia : EMA [Emerg Med Australas] 2022 Apr; Vol. 34 (2), pp. 252-262. Date of Electronic Publication: 2021 Oct 06.
Publication Type: Journal Article
Journal Info: Publisher: Blackwell Pub Country of Publication: Australia NLM ID: 101199824 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1742-6723 (Electronic) Linking ISSN: 17426723 NLM ISO Abbreviation: Emerg Med Australas Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Duwalage+KI%22">Duwalage KI</searchLink>; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.; Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Melbourne, Victoria, Australia.<br /><searchLink fieldCode="AU" term="%22Burkett+E%22">Burkett E</searchLink>; Emergency Department, Princess Alexandra Hospital, Brisbane, Queensland, Australia.; Queensland Government Clinical Excellence Division, Healthcare Improvement Unit, Brisbane, Queensland, Australia.<br /><searchLink fieldCode="AU" term="%22White+G%22">White G</searchLink>; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.; Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Melbourne, Victoria, Australia.<br /><searchLink fieldCode="AU" term="%22Wong+A%22">Wong A</searchLink>; Emergency Department, Princess Alexandra Hospital, Brisbane, Queensland, Australia.<br /><searchLink fieldCode="AU" term="%22Thompson+MH%22">Thompson MH</searchLink>; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.; Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
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  Data: <searchLink fieldCode="JN" term="%22101199824%22">Emergency medicine Australasia : EMA</searchLink> [Emerg Med Australas] 2022 Apr; Vol. 34 (2), pp. 252-262. <i>Date of Electronic Publication: </i>2021 Oct 06.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Blackwell+Pub%22">Blackwell Pub </searchLink><i>Country of Publication: </i>Australia <i>NLM ID: </i>101199824 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1742-6723 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2217426723%22">17426723 </searchLink><i>NLM ISO Abbreviation: </i>Emerg Med Australas <i>Subsets: </i>MEDLINE
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        Value: 10.1111/1742-6723.13875
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      – Code: eng
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              Text: 2022 Apr
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