Akademik Veri Yönetim Sistemi
BMJ OPEN, cilt.16, sa.5, 2026 (SCI-Expanded, Scopus)
Objective Traditional 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. Design A retrospective cohort study. Setting A large tertiary university hospital in T & uuml;rkiye. Participants Adult 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. Results A 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. Conclusion Machine 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.