Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.
Objective: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for deliri...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 27; no. 7; pp. 1383 - 1393 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2020
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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=146155416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146155416 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jul2020 vid: 27 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 146155416 146155416 NLM32968811 146155416 10.1093/jamia/ocaa113 NLM32968811 146155416 ppf: 1383 ppct: 10 formats: tig: atl: Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study. aug: au: Jauk, Stefanie Kramer, Diether Großauer, Birgit Rienmüller, Susanne Avian, Alexander Berghold, Andrea Leodolter, Werner Schulz, Stefan affil: Department of Information and Process Management, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), Graz, Austria sug: subj: Delirium Algorithms Risk Assessment Methods Hospitalization Human Aged Female Aged, 80 and Over Models, Theoretical Male Middle Age Prospective Studies ROC Curve Systems Analysis Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: Objective: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.Materials and Methods: Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting.Results: During clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded (r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry.Discussion: The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals.Conclusions: Our study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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