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Precision-Optimised Post-Stroke Prognoses
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DOI：10.1002/acn3.70077.png)
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Abstract
En 中文
Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by treating the post-stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known. This approach effectively shares prediction error equally among the patients, which is contrary to the long-held clinical intuition that some patients' outcomes are more predictable than other patients' outcomes. Here, we test that intuition empirically, by asking whether those ‘more predictable’ patients can be identified before their outcomes are known.
Keywords:
cognition
confidence
language
lesions
machine learning
stroke
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