eXplainable AI Allows Predicting Upper Limb Rehabilitation Outcomes in Sub-Acute Stroke Patients.

While stroke is one of the leading causes of disability, the prediction of upper limb (UL) functional recovery following rehabilitation is still unsatisfactory, hampered by the clinical complexity of post-stroke impairment. Predictive models leading to accurate estimates while revealing which features contribute most to the predictions are the key to unveil the mechanisms subserving the post-intervention recovery, prompting a new focus on individualized treatments and precision medicine in stroke. Machine learning (ML) and explainable artificial intelligence (XAI) are emerging as the enabling technology in different fields, being promising tools also in clinics. In this study, we had the twofold goal of evaluating whether ML can allow deriving accurate predictions of UL recovery in sub-acute patients, and disentangling the contribution of the variables shaping the outcomes. To do so, Random Forest equipped with four XAI methods was applied to interpret the results and assess the feature relevance and their consensus. Our results revealed increased performance when using ML compared to conventional statistical approaches. Moreover, the features deemed as the most relevant were concordant across the XAI methods, suggesting good stability of the results. In particular, the baseline motor impairment as measured by simple clinical scales had the largest impact, as expected. Our findings highlight the core role of ML not only for accurately predicting the individual outcome scores after rehabilitation, but also for making ML results interpretable when associated to XAI methods. This provides clinicians with robust predictions and reliable explanations that are key factors in therapeutic planning/monitoring of stroke patients.