Machine learning-based prediction models for falls in hospitalized patients: A systematic review and meta-analysis

To systematically review published machine learning models aimed at predicting the risk of falls among hospitalized patients.

A systematic review and meta-analysis.

According to the inclusion and exclusion criteria, we comprehensively searched the database PubMed, EMBASE, Web of Science and The Cochrane library from inception through November 14, 2023. Data extraction followed the CHARMS checklist, and bias risk and applicability were assessed using the PROBAST tool. A meta-analysis was performed utilizing Meta-disc software, with the area under the curve, sensitivity, and specificity serving as the effect measures. Heterogeneity was assessed through the Chi-square test and I² test.

A systematic search yielded a total of 2007 studies, 14 of which were selected following screening, and 13 of these studies were subjected to quantitative analyses. The incidence rate of inpatient falls ranged from 0.14 % to 50.69 %, with corresponding area under the curve (AUC) values varying between 0.57 and 0.99. Age, multiple drugs, emerged as the most frequently employed predictive factors. While the overall quality of the studies was considered satisfactory, a high risk of bias was identified, primarily attributed to insufficient reporting in the participant and analysis domains. The combined AUC of the 13 predictive models was 0.82, with a sensitivity of 0.69 (95 % CI [0.68-0.7]) and a specificity of 0.70 (95 % CI [0.70-0.71]), indicating robust discriminative performance.

Although machine learning models provide an emerging and promising method for predicting hospital falls, they require broader validation to ensure practical applicability. This review highlights the potential drawbacks of current methods, including high risk of bias and low reproducibility, and provides various recommendations on how to address these challenges.

Falls are a frequent and significant issue for patients in hospitals, often resulting in severe physical harm and longer hospital stays. This research offers a fresh approach and tool for medical professionals by thoroughly examining how machine learning models can predict falls, aiming to enhance personalized and precise fall risk management.