In Archives of physical medicine and rehabilitation ; h5-index 61.0
OBJECTIVE : To use machine learning-based methods in designing a predictive model of rehabilitation outcomes for post-acute hip-fractured patients.
DESIGN : A retrospective analysis using linear models, AdaBoost, CatBoost, ExtraTrees, K-Nearest Neighbors, RandomForest, Support vector machine, XGBoost, and voting of all models to develop and validate a predictive model.
SETTING : A university-affiliated 300-bed major post-acute geriatric rehabilitation center.
PARTICIPANTS : Consecutive hip-fractured patients (n=1625) admitted to an acute rehabilitation department.
MAIN OUTCOME MEASURES : The Functional Independence Measure (FIM) instrument, motor-FIM (mFIM), and the relative functional gain on mFIM (mFIM effectiveness) as a continuous and binary variable. Ten predictive models were created: base models (linear/logistic regression), and eight machine learning models (AdaBoost, CatBoost, ExtraTrees, K-Nearest Neighbors, RandomForest, Support vector machine, XGBoost, and a voting ensemble). R-squared was used to evaluate their performance in predicting a continuous outcome variable, and the area under the receiver operating characteristic curve was used to evaluate the binary outcome. A paired two-tailed t-test compared the results of the different models.
RESULTS : Machine learning-based models yielded better results than the linear/logistic regression models in predicting rehabilitation outcomes. The three most important predictors of the mFIM effectiveness score were: the MMSE, pre-fracture mFIM scores, and age; of the discharge mFIM score: the admission mFIM, MMSE and pre-fracture mFIM scores. The most contributing factors for favorable outcomes (mFIM effectiveness> median) with higher prediction confidence level were: high MMSE (25.7±2.8), high pre-facture mFIM (81.5±7.8) and high admission mFIM (48.6±8) scores. We present a simple prediction instrument for estimating the expected performance of post-acute hip-fractured patients.
CONCLUSION : Use of machine learning models to predict rehabilitation outcomes of post-acute hip-fractured patients is superior to the linear/logistic regression models. The higher the MMSE, pre-fracture mFIM and admission mFIM scores are, the higher the confidence levels of the predicted parameters.
Shtar Guy, Rokach Lior, Shapira Bracha, Nissan Ran, Hershkovitz Avital
Hip fracture, Machine learning, Outcome, Rehabilitation