  In this study, we utilized statistical analysis and machine learning methods
to examine whether rehabilitation exercises can improve patients post-stroke
functional abilities, as well as forecast the improvement in functional
abilities. Our dataset is patients' rehabilitation exercises and demographic
information recorded in the unstructured electronic health records (EHRs) data
and free-text rehabilitation procedure notes. We collected data for 265 stroke
patients from the University of Pittsburgh Medical Center. We employed a
pre-existing natural language processing (NLP) algorithm to extract data on
rehabilitation exercises and developed a rule-based NLP algorithm to extract
Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility
(BM) and applied cognitive (AC) domains, from procedure notes. Changes in
AM-PAC scores were classified based on the minimal clinically important
difference (MCID), and significance was assessed using Friedman and Wilcoxon
tests. To identify impactful exercises, we used Chi-square tests, Fisher's
exact tests, and logistic regression for odds ratios. Additionally, we
developed five machine learning models-logistic regression (LR), Adaboost
(ADB), support vector machine (SVM), gradient boosting (GB), and random forest
(RF)-to predict outcomes in functional ability. Statistical analyses revealed
significant associations between functional improvements and specific
exercises. The RF model achieved the best performance in predicting functional
outcomes. In this study, we identified three rehabilitation exercises that
significantly contributed to patient post-stroke functional ability improvement
in the first two months. Additionally, the successful application of a machine
learning model to predict patient-specific functional outcomes underscores the
potential for precision rehabilitation.
