Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.

ABSTRACT: Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work was to develop a ML model to predict 30-day all-cause rehospitalizations based on the French hospital medico-administrative database.This was a retrospective cohort study of all discharges in the year 2015 from acute-care inpatient hospitalizations in a tertiary-care university center comprising 4 French hospitals. The study endpoint was unplanned 30-day all-cause rehospitalization. Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB), and neural networks (NN) were applied to the coll

SUBMITTER: Jaotombo F 

PROVIDER: S-EPMC7717815 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature