Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study.

ABSTRACT: Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient-safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi-view ensemble learning and model-based missing data imputation method. As input, over two thousand inpatient fall patients' demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clin

SUBMITTER: Wang L 

PROVIDER: S-EPMC6908660 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature