In what universe do you live where predicting failure to recover has any usefulness at all for stroke survivors? I'd fire all your asses in an instant.
eXplainable AI allows predicting upper limb rehabilitation outcomes in sub-acute stroke patients
Abstract:
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 to derive 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 a
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 follow-up 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.
Published in: IEEE Journal of Biomedical and Health Informatics ( Early Access )
Publisher: IEEE
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