Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data...30th Medical Informatics Europe Conference
Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine learning algorithms (Gradient Boosting, Random Forest, and Neural Network) for predicting the risk of all-cause, 30-day hospital readmission using data...
| Publicado en: | Studies in Health Technology & Informatics Vol. 270; pp. 547 - 552 |
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| Autores principales: | , , , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2020
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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=144555300&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144555300 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2020 vid: 270 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 144555300 144555300 144555300 10.3233/SHTI200220 144555300 ppf: 547 ppct: 5 formats: tig: atl: Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data...30th Medical Informatics Europe Conference aug: au: DHALLUIN, Thibault BANNAY, Aurélie LEMORDANT, Pierre SYLVESTRE, Emmanuelle CHAZARD, Emmanuel CUGGIA, Marc BOUZILLE, Guillaume affil: Univ Rennes, CHU Rennes, Inserm, LTSI – UMR 1099, F-35000 Rennes, France. sug: subj: Readmission Data Warehouse Socioeconomic Factors Human Congresses and Conferences Logistic Regression Algorithms Machine Learning Medical Informatics Male Female Adult Middle Age Aged Aged, 80 and Over Descriptive Statistics Length of Stay Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine learning algorithms (Gradient Boosting, Random Forest, and Neural Network) for predicting the risk of all-cause, 30-day hospital readmission using data from the clinical data warehouse of Rennes and from other sources. The dataset included hospital stays based on the criteria of the French national methodology for the 30-day readmission rate (i.e., patients older than 18 years, geolocation, no iterative stays, and no hospitalization for palliative care), with a similar pre-processing for all algorithms. We calculated the area under the ROC curve (AUC) for 30-day readmission prediction by each model. In total, we included 259114 hospital stays, with a readmission rate of 8.8%. The AUC was 0.61 for the Logistic Regression, 0.69 for the Gradient Boosting, 0.69 for the Random Forest, and 0.62 for the Neural Network model. We obtained the best performance and reproducibility to predict readmissions with Random Forest, and found that the algorithms performed better when data came from different sources. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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