用大模型提升病历文本的疾病编码准确率
Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding
- 直接用LLAMA做分类，或生成文本特征再输入神经网络
- 在ICD编码任务中优于现有方法，提升分类效果
- 适合医疗信息化、临床数据标注场景使用
由于医学记录的复杂性，从出院小结中准确分类国际疾病分类（ICD）代码极具挑战。本文探索了大型语言模型（LLM），特别是LLAMA架构，在两种方法中的应用：作为直接分类器，或在多滤波残差卷积神经网络（MultiResCNN）框架中生成增强文本表示。通过与现有先进方法对比评估，结果表明LLAMA能通过提供深层上下文理解，显著改善分类性能。
原文摘要 · Abstract (English)
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.
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