Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding

TL;DR

This paper investigates the application of Large Language Models, particularly LLAMA, to improve automatic ICD coding from medical summaries through direct classification and enriched text representations, outperforming existing methods.

Contribution

It introduces a novel approach combining LLAMA with MultiResCNN for enhanced ICD code classification, demonstrating significant performance improvements.

Findings

LLAMA-based methods outperform state-of-the-art approaches

Enhanced text representations improve classification accuracy

Deep contextual insights from LLAMA aid in medical text understanding

Abstract

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.