Clinical coding is the task of assigning a set of alphanumeric codes, referred to as ICD, to a medical event based on the context captured in a clinical narrative. The latest version of ICD, ICD-10, includes more than 70,000 codes.

This talk will discuss a novel approach for automatic ICD coding by reformulating the extreme multi-label problem into a simpler multi-class problem using a hierarchical solution. We made this approach viable through extensive data collection to acquire phrase-level human coder annotations to supervise our models on learning the specific relations between the input text and predicted ICD code set.