Beyond ICD Codes: Semantic IDs for Generative Medical Coding
Generative approaches to automatic International Classification of Diseases (ICD) coding use large language models (LLMs) to predict ICD codes from clinical documents.
ProofPaper ↗
Key points
- However, original ICD code strings are not ideal generation targets because the ICD hierarchy does not always reflect clinical relationships, and LLMs struggle to interpret and generate these codes.
- We introduce SemICD, a framework that trains LLMs to generate Semantic IDs (SIDs) instead.
- SemICD constructs SIDs by hierarchically clustering embeddings of textual code profiles, allowing semantically related codes to share prefixes while each complete SID uniquely identifies one ICD code.
- To align the SID tokens with clinical semantics, we employ two auxiliary tasks for bidirectional generation between textual code profiles and SIDs.
Sources (1)
- [1]Beyond ICD Codes: Semantic IDs for Generative Medical CodingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 11:20 PM
Generative approaches to automatic International Classification of Diseases (ICD) coding use large language models (LLMs) to predict ICD codes from clinical documents.
However, original ICD code strings are not ideal generation targets because the ICD hierarchy does not always reflect clinical relationships, and LLMs struggle to interpret and generate these codes.
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