Deep Learning for Predicting Rehabilitation Success: Advancing Clinical and Patient-Reported Outcome Modeling

Predicting rehabilitation outcomes is essential for guiding clinical decisions and improving patient care. Traditional machine learning methods, while effective, are often limited in their ability to capture complex, nonlinear relationships in data. This study investigates the application of deep learning techniques, including hybrid Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to predict rehabilitation success based on clinical and patient-reported outcome measures (CROMs and PROMs). Using a dataset of 1047 rehabilitation patients encompassing diverse musculoskeletal conditions and treatment protocols, we compare the performance of deep learning models with previously established machine learning approaches such as Random Forest and Extra Trees classifiers. Our findings reveal that deep learning significantly enhances predictive performance. The weighted F1-score for direct classification improved from 65% to 74% using a CNN-RNN architecture, and the mean absolute error (MAE) for regression-based success metrics decreased by 12%, translating to more precise estimations of functional recovery. These improvements hold clinical significance as they enhance the ability to tailor rehabilitation interventions to individual patient needs, potentially optimizing recovery timelines and resource allocation. Moreover, attention mechanisms integrated into the deep learning models provided improved interpretability, highlighting key predictors such as age, range of motion, and PROM indices. This study underscores the potential of deep learning to advance outcome prediction in rehabilitation, offering more precise and interpretable tools for clinical decision-making. Future work will explore real-time applications and the integration of multimodal data to further refine these models.