ArticleScientific reports2025
Machine learning models predicting inpatient falls.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Fractures After Inpatient Falls in a Tertiary Rehabilitation Hospital: Associated Factors, Performance of Fall-Risk Scores, and Orthopaedic Outcomes.Journal of clinical medicine · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data.Nursing reports (Pavia, Italy) · 2026Article
- Enhancing multimodal inpatient fall prediction via nursing statement integration within the OMOP common data model.Scientific reports · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Accurate prediction of inpatient fall risk is crucial for preventing injuries and improving patient safety in hospitals. The widely used Hester-Davis score (HD) lacks precision, highlighting the need for more advanced models. This retrospective study analyzed 46,695 patients from 17 hospitals across four U.S. states, admitted between January 2018 and July 2022, including 4245 fallers. Four dynamic machine learning models were developed using HD variables alone and in combination with socio-demographics, comorbidities, physiological measures, medications, and timeseries data updated at 8- and 24 h intervals. Among the models, Extreme Gradient Boosting model outperformed HD, achieving an AUC of 0.87 (95% CI 0.86-0.88), compared to HD AUCs of 0.57 (95% CI 0.56-0.58) and 0.62 (95% CI 0.59-0.61) at thresholds of 7 and 20. Key predictors included pre-existing neurological conditions, behavioral abnormalities, oxygen saturation, heart rate, and IV furosemide use. Prospective validation is required for real-time implementation in clinical practice.
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Registered trials
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