Is Regular Re-Training of a Predictive Delirium Model Necessary After Deployment in Routine Care?
Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms have already proved their significance in healthca...
| Publicado en: | Studies in Health Technology & Informatics Vol. 260; pp. 186 - 192 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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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=136670411&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136670411 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 260 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 136670411 136670411 136670411 10.3233/978-1-61499-971-3-186 136670411 ppf: 186 ppct: 6 formats: tig: atl: Is Regular Re-Training of a Predictive Delirium Model Necessary After Deployment in Routine Care? aug: au: VEERANKI, Sai Pavan Kumar KRAMER, Diether HAYN, Dieter JAUK, Stefanie EGGERTH, Alphons QUEHENBERGER, Franz LEODOLTER, Werner SCHREIER, Günter affil: AIT Austrian Institute of Technology, Graz, Austria sug: subj: Delirium Risk Factors Models, Theoretical Machine Learning Risk Assessment Human Retrospective Design Electronic Health Records ROC Curve Hospitalization ab: Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms have already proved their significance in healthcare research, remains a challenge translation and dissemination of fully automated prediction algorithms from research to decision support at the point of care. In this paper, we address the effect of changes in the characteristics of data over time on the performance of deployed models for the use case of predicting delirium in hospitalised patients. We have analysed the stability of models trained with subsets of data from one single year (2012, 2013...2016, respectively), and tested the models with data from 2017. Our results show that in the case of delirium prediction, the models were stable over time, indicating that re-training the models is not necessary e.g. once per year might be more than sufficient. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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