Precision-Optimised Post-Stroke Prognoses

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.

Drawing on lesion location and demographic data, we use ensemble classifiers to predict the presence of a variety of different language impairments in a large sample of stroke patients. We tune these models to maximise their Positive Predictive Value (or precision): that is, the probability that patients assigned to a class are really members of that class. We test whether those tuned models have high precision on independent data.

Precision-tuned models might only classify a subset of patients, but for that reduced set, the classifications are very likely to be correct: typically > 90% and sometimes > 95%. Small reductions of target precision could rapidly raise the proportion of patients for whom 'high enough precision' predictions can be made.

High precision prognoses are possible when predicting language outcomes after stroke. Providing such predictions for subsets of patients might be a reasonable intermediate step on the way to providing them for all.