Predicting Falls in Long-term Care Facilities: Machine Learning Study.

ABSTRACT:

Background

Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities.

Objective

The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care.

Methods

This retrospective study obtained EHR data (2007-2021) from Juniper Communities' proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall predict

SUBMITTER: Thapa R 

PROVIDER: S-EPMC9015781 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature