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A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention

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.

Accelerating Healthcare Innovation: Finding Answers Faster Using NLP Models and Dedicated AI Compute

The pace of new research in the healthcare and pharmaceutical industries is astounding, and while this progress promises to uncover new disease...