biostudies-literatureUnknown5(2)Thapa R<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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 predictJMIR aginge35373https://www.ebi.ac.uk/biostudies/studies/S-EPMC9015781biostudies-literaturePredicting Falls in Long-term Care Facilities: Machine Learning Study.PMC9015781Barnes GKatzmann LHoffman JDas RCalvert JMao QGarikipati AHurtado MThapa RShokouhi SfalsePredicting Falls in Long-term Care Facilities: Machine Learning Study.<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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 predict2022-01-01T00:00:00Z2022 Apr2025-04-19T17:38:43.547Z2025-02-19T03:18:46.223ZS-EPMC90157813536314610.2196/35373