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.

翻译：准确从医疗出院摘要中分类国际疾病分类（ICD）代码面临挑战，这源于医疗文档的复杂性。本文探索了使用大型语言模型（LLM），特别是LLAMA架构，通过两种方法增强ICD代码分类：直接作为分类器应用，以及作为多滤波器残差卷积神经网络（MultiResCNN）框架内增强文本表示的生成器。我们通过将这些方法与最先进方法进行比较来评估其效果，结果表明LLAMA通过提供对医疗文本的深度上下文洞察，具有显著改善分类结果的潜力。