Fig. 2: Average confusion matrices for the full model using decision thresholds that maximize Youden’s index across different text-cutoff thresholds.
From: Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data
Confusion matrices for the full model are presented for three different text-cutoff thresholds (500, 1000, and 5000 characters) in panels (a), (b), and (c), respectively. For each cutoff, the optimal classification threshold was determined by maximizing Youden’s index (i.e., maximizing the sum of sensitivity and specificity, which corresponds to the sum of the row-normalized diagonal elements). Each cell in the matrices represents the proportion of cases for the true class (expressed as a percentage), with the axes labeled “P:C < 1.8” and “P:C ≥ 1.8” indicating the binary classification outcome for the penumbra-to-core ratio.
