The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision‐making.
Objective: To further develop and refine an Emergency Department (ED) in‐patient admission prediction model using machine learning techniques. Methods: This was a retrospective analysis of state‐wide ED data from New South Wales, Australia. Six classification algorithms (Bayesian networks, decision...
| Publicado en: | Emergency Medicine Australasia Vol. 31; no. 3; pp. 429 - 436 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136662182&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136662182 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17426731 Z3W jtl: Emergency Medicine Australasia issn: 17426731 maglogo: Y pubinfo: dt: Jun2019 vid: 31 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 136662182 136662182 136662182 10.1111/1742-6723.13199 136662182 ppf: 429 ppct: 7 formats: tig: atl: The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision‐making. aug: au: Rendell, Kathryn Koprinska, Irena Kyme, Andre Ebker‐White, Anja A Dinh, Michael M affil: School of Aerospace, Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney New South Wales, Australia sug: subj: Decision Making, Clinical Machine Learning Risk Assessment Emergency Service Human Retrospective Design Australia Logistic Regression Scales Algorithms Confidence Intervals Descriptive Statistics Inpatients Patient Admission Decision Support Systems, Clinical ab: Objective: To further develop and refine an Emergency Department (ED) in‐patient admission prediction model using machine learning techniques. Methods: This was a retrospective analysis of state‐wide ED data from New South Wales, Australia. Six classification algorithms (Bayesian networks, decision trees, logistic regression, naïve Bayes, neural networks and nearest neighbour) and five feature selection techniques (none, manual, correlation‐based, information gain and wrapper) were examined. Presenting problem was categorised using broad (n = 20) and specific (n = 100) representations. Models were evaluated based on Area Under the Curve (AUC) and accuracy. The results were compared with the Sydney Triage to Admission Risk Tool (START), which uses logistic regression and six manually selected features. Results: Sixty admission prediction models were trained and validated using data from 1 721 294 patients. Under the broad representation of presenting problem, the nearest neighbour algorithm with manual feature selection had the best AUC of 0.8206 (95% CI ±0.0006), while the decision tree with no feature selection had the best accuracy of 74.83% (95% CI ±0.065). Under the specific representation, almost all models improved; the nearest neighbour with information gain feature selection had the best AUC of 0.8267 (95% CI ±0.0006), while the decision tree with wrapper or no feature selection had the best accuracy of 75.24% (95% CI ±0.064). Eleven of the machine learning models had slightly better AUC than the START model. Conclusion: Machine learning methods demonstrate similar performance to logistic regression for ED disposition prediction models using basic triage information. This should be investigated further, especially for larger data sets with more complex clinical information. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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