A neural architecture for automated ICD coding

Abstract

The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases. Medical coding - which assigns a subset of ICD codes to a patient visit - is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error-prone. In this paper, we build a neural architecture for automated coding. It takes the diagnosis descriptions (DDs) of a patient as inputs and selects the most relevant ICD codes. This architecture contains four major ingredients: (1) tree-of-sequences LSTM encoding of code descriptions (CDs), (2) adversarial learning for reconciling the different writing styles of DDs and CDs, (3) isotonic constraints for incorporating the importance order among the assigned codes, and (4) attentional matching for performing many-to-one and one-to-many mappings from DDs to CDs. We demonstrate the effectiveness of the proposed methods on a clinical datasets with 59K patient visits.