Comparative analysis of machine learning approaches for predicting frequent emergency department visits.
Background: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users. Method: Korean Health Panel data from 2008 to 2015 were used for this study. Indivi...
| Publicado en: | Health Informatics Journal Vol. 28; no. 2; pp. 1 - 19 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Apr-Jun2022
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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=191301710&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191301710 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Apr-Jun2022 vid: 28 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 191301710 10.1177/14604582221106396 191301710 ppf: 1 ppct: 18 formats: tig: atl: Comparative analysis of machine learning approaches for predicting frequent emergency department visits. aug: au: Safaripour, Razieh "June" Lim, Hyun Ja affil: Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada sug: ab: Background: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users. Method: Korean Health Panel data from 2008 to 2015 were used for this study. Individuals with four or more visits per year were considered frequent ED users. Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) as well as two ensemble models, namely Bagging and Voting, were trained and tested to examine their predictive performance. Results: The ML classification algorithms identified frequent ED users with high precision (90%–98%) and sensitivity (87%–91%), whereas LR showed fair precision (65%) and sensitivity (67%). The ML algorithms showed a high area under the curve (AUC) values from 89% for SVM to 96% for Random Forest, while LR showed the lowest AUC (65%). The classification error varied among algorithms; LR had the highest classification error (24.07%) while RF had the least (3.8%). Conclusions: Results show that ML classification algorithms are robust techniques to predict frequent ED users, and the variables in administrative health panels are reliable indicators for this purpose. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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