Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction
ConclusionMachine learning that is applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method in the pre-interventional prediction of stroke prognosis. Interpretable models allow for exploring which initial features contribute the most to post-thrombectomy outcome prediction overall and for each individual patient outcome.
Source: Frontiers in Neurology - Category: Neurology Source Type: research
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