Thirty-day hospital readmission prediction model based on common data model with weather and air quality data.

ABSTRACT: Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainf

SUBMITTER: Ryu B 

PROVIDER: S-EPMC8639801 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature