Explainable Clinical Decision Support from Text
Jinyue Feng, Chantal Shaib, Frank Rudzicz · 2020
Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians.Further, free-text medical notes may contain information not immediately available in structured variables.We propose a hierarchical CNNtransformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction on the English MIMIC-III dataset, respectively.We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis.Finally, we outline a protocol to evaluate model usability in a clinical decision support context.From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.