Machine learning modeling for predicting hospital readmission following lumbar laminectomy

Abstract

In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy >95% when using all variables and >79% when using only predischarge variables. A model capable of predicting 40% of readmitted patients was created using only the variables known predischarge. This investigation is important in its provision of data that will assist the development of predictive models for readmission as well as interventions to prevent readmission in high-risk patients.