Zero-Shot ATC Coding with Large Language Models for Clinical Assessments

• URL: http://arxiv.org/abs/2412.07743v1
• Date: Tue, 10 Dec 2024 18:43:02 GMT
• Title: Zero-Shot ATC Coding with Large Language Models for Clinical Assessments
• Authors: Zijian Chen, John-Michael Gamble, Micaela Jantzi, John P. Hirdes, Jimmy Lin,
• Abstract summary: Manual assignment of Anatomical Therapeutic Chemical codes to prescription records is a significant bottleneck. We develop a practical approach using locally deployable large language models (LLMs) We evaluate our approach using GPT-4o as an accuracy ceiling and focus development on open-source Llama models suitable for privacy-sensitive deployment.
• Score: 40.72273945475456
• License:
• Abstract: Manual assignment of Anatomical Therapeutic Chemical (ATC) codes to prescription records is a significant bottleneck in healthcare research and operations at Ontario Health and InterRAI Canada, requiring extensive expert time and effort. To automate this process while maintaining data privacy, we develop a practical approach using locally deployable large language models (LLMs). Inspired by recent advances in automatic International Classification of Diseases (ICD) coding, our method frames ATC coding as a hierarchical information extraction task, guiding LLMs through the ATC ontology level by level. We evaluate our approach using GPT-4o as an accuracy ceiling and focus development on open-source Llama models suitable for privacy-sensitive deployment. Testing across Health Canada drug product data, the RABBITS benchmark, and real clinical notes from Ontario Health, our method achieves 78% exact match accuracy with GPT-4o and 60% with Llama 3.1 70B. We investigate knowledge grounding through drug definitions, finding modest improvements in accuracy. Further, we show that fine-tuned Llama 3.1 8B matches zero-shot Llama 3.1 70B accuracy, suggesting that effective ATC coding is feasible with smaller models. Our results demonstrate the feasibility of automatic ATC coding in privacy-sensitive healthcare environments, providing a foundation for future deployments.

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