Résumé
Context: We present a post-hoc approach to improve the recall of ICD classification. Method: The proposed method can use any classifier as a backbone and aims to calibrate the number of codes returned per document. We test our approach on a new stratified split of the MIMIC-III dataset. Results: When returning 18 codes on average per document we obtain a recall that is 20% better than a classic classification approach.