Prioritizing interpretability in an algorithm for thrombolysis selection following stroke

Selecting patients who would benefit from thrombolysis following acute ischemic stroke can be achieved by a predictive algorithm. Algorithm interpretability is critical for widespread clinical uptake.
The advanced version of classical-k-nearest neighbors classification algorithm (KNN) outperformed the classical KNN algorithm in terms of predictive power (P=0.019). The advanced algorithm identified clinical features (onset time, diabetes, and baseline National Institutes of Health Stroke Scale scores) with significant effects on the output.
The authors concluded that advanced classical KNN offers an accurate and easy to interpret algorithm for the selection of patients for thrombolysis.