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<p>Early-stage rehabilitation is crucial for the functional recovery of patients with proximal femur fractures. Predicting functional prognosis at such an early stage can simplify the process of planning for transfers and discharge destinations, as well as setting rehabilitation goals. The current study aimed to develop a model using machine learning to predict the functional prognosis of patients with proximal femur fractures based on parameters at the time of hospital admission. Our research utilized a dataset from 3,088 proximal femur fracture cases recorded in the Japan Association of Rehabilitation Database. The dependent variable was the level of independence in daily living (ADL) activities at discharge, categorized into nine classes. A regression model was implemented by approximating the dependent variable to a continuous value. Accuracy and Quadratic Weighted Kappa (QWK) were used to evaluate the prediction accuracy, and SHAP (SHapley Additive exPlanations) values were used to evaluate the model’s predictive explainability. The machine learning model exhibited an Accuracy of 0.340 and a QWK of 0.657, indicating solid agreement. The top parameters according to SHAP values were the total Functional Independence Measure (FIM) score and the level of independence in ADL in elderly with dementia. We developed a machine learning model capable of predicting the independence level in daily activities at discharge, utilizing patient data available at hospital admission for those suffering from proximal femur fractures.</p>

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Development of a machine learning-based model for predicting the functional outcome of patients with proximal femur fractures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Kyohei Nozawa,
  • Satoshi Maki,
  • Issei Tanaka,
  • Kazuhide Inage,
  • Yasuhiro Shiga,
  • Masahiro Inoue,
  • Yawara Eguchi,
  • Takeo Furuya,
  • Junichi Nakamura,
  • Shigeo Hagiwara,
  • Yuya Kawarai,
  • Seiji Ohtori,
  • Sumihisa Orita

摘要

Early-stage rehabilitation is crucial for the functional recovery of patients with proximal femur fractures. Predicting functional prognosis at such an early stage can simplify the process of planning for transfers and discharge destinations, as well as setting rehabilitation goals. The current study aimed to develop a model using machine learning to predict the functional prognosis of patients with proximal femur fractures based on parameters at the time of hospital admission. Our research utilized a dataset from 3,088 proximal femur fracture cases recorded in the Japan Association of Rehabilitation Database. The dependent variable was the level of independence in daily living (ADL) activities at discharge, categorized into nine classes. A regression model was implemented by approximating the dependent variable to a continuous value. Accuracy and Quadratic Weighted Kappa (QWK) were used to evaluate the prediction accuracy, and SHAP (SHapley Additive exPlanations) values were used to evaluate the model’s predictive explainability. The machine learning model exhibited an Accuracy of 0.340 and a QWK of 0.657, indicating solid agreement. The top parameters according to SHAP values were the total Functional Independence Measure (FIM) score and the level of independence in ADL in elderly with dementia. We developed a machine learning model capable of predicting the independence level in daily activities at discharge, utilizing patient data available at hospital admission for those suffering from proximal femur fractures.