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Objective <p>Traditional risk assessment tools were commonly used for the prediction of falls in clinical practice. However, their tendency toward over-sensitivity addressed its limitations for hospitalized cancer patients. This study aimed to identify risk factors associated with falls in hospitalized patients and to develop a predictive risk model using machine learning algorithms specifically for cancer patients, to optimize the clinical assessment of fall risk.</p> Methods <p>Using data from 298 cancer patients admitted to Shandong Cancer Hospital between January 2023 and December 2024 were included. We described the patient status through three aspects: clinical indicators, laboratory indicators and subjective evaluation indicators. The model was evaluated for performance using five-fold cross-validation. Least absolute shrinkage and selection operator (LASSO) regression was used for initiatory variable selection. We constructed predictive models using one baseline method and three machine learning methods, incorporating key clinical features, and selected the optimal model based on a review of categorical performance metrics. Additionally, Shapley Additive Explanations (SHAP) was used to interpret the predictive models and rank the importance of risk factors.</p> Results <p>Among 298 cancer patients assessed by the Morse Fall Scale, only 115 (39%) experienced falls during hospitalization. Based on LASSO regression and statistical analysis, 20 key predictors were identified from 30 candidate features. The Support Vector Machine model outperformed the other algorithms (Naive Bayes, Multivariate Logistic Regression, and Random Forest), achieving the best performance in both the low-risk and high-risk populations evaluated by the scale, with a composite F1-score of 0.87. SHAP interpretability analysis revealed that medication use, diagnostic findings, and treatment approaches were major risk factors, while the number of comorbidities further increased susceptibility to falls. The model’s precision in stratifying dynamic risks, particularly for high-risk subgroups (e.g., patients with brain metastases or neurotoxic regimens), highlights its clinical utility.</p> Conclusion <p>This study establishes a clinically interpretable, machine learning-driven fall risk prediction model that dynamically integrates cancer-specific variables. Implementation of this model could enhance risk stratification accuracy, guide personalized intervention strategies, and optimize resource allocation in clinical oncology care settings.</p> Clinical trial number <p>Not applicable.</p>

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Dynamic fall risk prediction in hospitalized cancer patients: development and validation of a machine learning model using multidimensional clinical data to overcome over-sensitivity in traditional scales

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月14日

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  • Huan Zhang,
  • Yifei An,
  • Min Song,
  • Yingtao Meng

摘要

Objective

Traditional risk assessment tools were commonly used for the prediction of falls in clinical practice. However, their tendency toward over-sensitivity addressed its limitations for hospitalized cancer patients. This study aimed to identify risk factors associated with falls in hospitalized patients and to develop a predictive risk model using machine learning algorithms specifically for cancer patients, to optimize the clinical assessment of fall risk.

Methods

Using data from 298 cancer patients admitted to Shandong Cancer Hospital between January 2023 and December 2024 were included. We described the patient status through three aspects: clinical indicators, laboratory indicators and subjective evaluation indicators. The model was evaluated for performance using five-fold cross-validation. Least absolute shrinkage and selection operator (LASSO) regression was used for initiatory variable selection. We constructed predictive models using one baseline method and three machine learning methods, incorporating key clinical features, and selected the optimal model based on a review of categorical performance metrics. Additionally, Shapley Additive Explanations (SHAP) was used to interpret the predictive models and rank the importance of risk factors.

Results

Among 298 cancer patients assessed by the Morse Fall Scale, only 115 (39%) experienced falls during hospitalization. Based on LASSO regression and statistical analysis, 20 key predictors were identified from 30 candidate features. The Support Vector Machine model outperformed the other algorithms (Naive Bayes, Multivariate Logistic Regression, and Random Forest), achieving the best performance in both the low-risk and high-risk populations evaluated by the scale, with a composite F1-score of 0.87. SHAP interpretability analysis revealed that medication use, diagnostic findings, and treatment approaches were major risk factors, while the number of comorbidities further increased susceptibility to falls. The model’s precision in stratifying dynamic risks, particularly for high-risk subgroups (e.g., patients with brain metastases or neurotoxic regimens), highlights its clinical utility.

Conclusion

This study establishes a clinically interpretable, machine learning-driven fall risk prediction model that dynamically integrates cancer-specific variables. Implementation of this model could enhance risk stratification accuracy, guide personalized intervention strategies, and optimize resource allocation in clinical oncology care settings.

Clinical trial number

Not applicable.