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Machine Learning-Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter

Takuya Nishino¹, Kotone Matsuyama^1,2, Yasuo Miyagi^3,4

¹Department of Health Policy and Management, Nippon Medical School, Tokyo, Japan.

JMIR Medical Informatics

|December 4, 2025

Summary

This summary is machine-generated.

Machine learning models, specifically CatBoost and LGBM, show promise in predicting in-hospital falls among older adults. These advanced tools can identify high-risk patients and guide targeted interventions to improve patient safety.

Keywords:

calibration falls hospitals inpatients machine learning predictive model risk assessment

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Area of Science:

Geriatric Medicine
Health Informatics
Machine Learning in Healthcare

Background:

Hospitalized patients, particularly older adults, are at high risk for falls, leading to extended stays and increased costs.
Traditional fall risk assessments may not fully capture the complexity of multifactorial fall risks.

Purpose of the Study:

To develop and validate machine learning (ML) models for predicting in-hospital falls in patients aged 65 and older.
To evaluate the predictive performance (discrimination and calibration) of different ML models.

Main Methods:

A retrospective analysis of 83,917 inpatients using Diagnosis Procedure Combination data and laboratory results.
Extraction of demographic, clinical, functional, and pharmacological variables, with 30 key features selected.
Construction and comparison of four models: logistic regression, extreme gradient boosting, light gradient boosting machine (LGBM), and categorical boosting (CatBoost), with techniques to address class imbalance.

Main Results:

Falls occurred in 2.6% of patients. CatBoost demonstrated the highest F1-score (0.189) and area under the precision-recall curve (0.112).
LGBM showed the best calibration (slope 0.964) with good discrimination (F1-score 0.182).
Key predictors included low albumin, impaired transfer ability, and use of sedative-hypnotics or diabetes medications. Toileting-related falls were most common (49.2%), peaking early morning.

Conclusions:

CatBoost and LGBM models offer clinically valuable prediction performance for in-hospital falls.
CatBoost is suitable for identifying high-risk patients, while LGBM aids in setting probability-based intervention thresholds.
Integration into electronic health records can enable real-time risk scoring and targeted interventions, with future work focusing on dynamic data incorporation.