ArticleBMJ open2026
Prediction of inpatient falls and key predictors using machine learning applied to electronic health records: a retrospective cohort study in a tertiary hospital in Türkiye.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTraditional fall risk tools are often inaccurate and burdensome. This study aims to develop a predictive model for inpatient falls and identify the most influential variables using machine learning applied to electronic health record data.
designA retrospective cohort study.
settingA large tertiary university hospital in Türkiye.
participantsAdult patients (≥18 years) hospitalised in a university hospital between January 2017 and June 2023. PRIMARY OUTCOME MEASURES: Occurrence of inpatient falls recorded in incident reporting systems.
resultsA total of 518 fallers were identified and compared with 3121 non-fallers. Fallers were significantly older (median 68.5 vs 64 years, p<0.001), had longer hospital stays (16 vs 12 days, p<0.001) and higher comorbidity burden (p<0.001). They were also more likely to receive high fall-risk medications (86% vs 76%, p<0.001) and had a higher prevalence of mental disorders (26.3% vs 17.2%, p<0.001). The random forest quantile classifier demonstrated the highest discrimination (area under the curve (AUC) 0.821, 95% CI 0.781 to 0.861), with balanced sensitivity (0.799, 95% CI 0.734 to 0.867) and specificity (0.722, 95% CI 0.691 to 0.755). Pairwise comparisons using DeLong's test showed significantly higher AUC compared with several models (adjusted p=0.001), while differences with eXtreme Gradient Boosting were not significant (p=0.240). Key predictors included comorbidity burden, age, number of fall-risk-increasing medications and laboratory variables such as eosinophil and basophil counts, blood urea nitrogen, sodium and red blood cell count.
conclusionMachine learning models using electronic health data can predict inpatient falls and reveal key risk factors. The random forest quantile classifier offers a promising approach for improving fall risk prediction in imbalanced clinical datasets.
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