Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population.

Journal: The American journal of medicine

Published Date: Apr 6, 2024

Abstract

BACKGROUND: Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments.

Authors

Ranel Loutati

Leviev Cardiovascular Institute, Sheba Medical Center, Ramat Gan, Israel.
Arie Ben-Yehuda

Department of Medicine, Hadassah Medical Center Faculty of Medicine, Hebrew University, Jerusalem, Israel.
Shai Rosenberg

Institut du Cerveau et de la Moelle épinière, INSERM U1127, CNRS UMR7225, Paris, France shair@hadassah.org.il ahmed.idbaih@aphp.fr.
Yakir Rottenberg

Sharett Institute of Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel.

Keywords

Aged Aged, 80 and over Algorithms Female Humans Machine Learning Male Patient Readmission Retrospective Studies Risk Assessment Risk Factors ROC Curve

External Resources

View on PubMed Access via DOI PubMed (38588939)

Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population.

Abstract

Authors

Keywords

External Resources

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