Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding
It addresses the challenge of complex medical documentation for healthcare professionals, but appears incremental as it builds on existing LLM and MultiResCNN methods.
This paper tackles the problem of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries by using Large Language Models (LLMs), specifically LLAMA, to enhance classification outcomes through direct application and enriched text representations.
Addressing the complexity of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries is challenging due to the intricate nature of medical documentation. This paper explores the use of Large Language Models (LLM), specifically the LLAMA architecture, to enhance ICD code classification through two methodologies: direct application as a classifier and as a generator of enriched text representations within a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) framework. We evaluate these methods by comparing them against state-of-the-art approaches, revealing LLAMA's potential to significantly improve classification outcomes by providing deep contextual insights into medical texts.