Using Machine Learning to Predict Rehabilitation Outcomes in Postacute Hip Fracture Patients

Guy Shtar, Lior Rokach, Bracha Shapira, Ran Nissan, Avital Hershkovitz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Objective: To use machine learning-based methods in designing a predictive model of rehabilitation outcomes for postacute hip fracture 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 postacute geriatric rehabilitation center. Participants: Consecutive hip fracture patients (N=1625) admitted to an postacute rehabilitation department. Main Outcome Measures: The 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 8 machine learning models (AdaBoost, CatBoost, ExtraTrees, K-Nearest Neighbors, RandomForest, Support vector machine, XGBoost, and a voting ensemble). R2 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 2-tailed t test compared the results of the different models. Results: Machine learning-based models yielded better results than the linear and logistic regression models in predicting rehabilitation outcomes. The 3 most important predictors of the mFIM effectiveness score were the Mini Mental State Examination (MMSE), prefracture mFIM scores, and age. The 3 most important predictors of the discharge mFIM score were the admission mFIM, MMSE, and prefracture 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 prefacture 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 postacute hip fracture patients. Conclusions: The use of machine learning models to predict rehabilitation outcomes of postacute hip fracture patients is superior to linear and logistic regression models. The higher the MMSE, prefracture mFIM, and admission mFIM scores are, the higher the confidence levels of the predicted parameters.

Original languageEnglish
Pages (from-to)386-394
Number of pages9
JournalArchives of Physical Medicine and Rehabilitation
Volume102
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • Hip fracture
  • Machine learning
  • Rehabilitation
  • Subacute care

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