ICD编码理由的评估与大语言模型引导的学习
Evaluation and LLM-Guided Learning of ICD Coding Rationales
AI总结：
本研究评估ICD编码中理由的可解释性，通过构建多粒度数据集和LLM引导学习方法，提升理由生成的合理性和模型决策的忠实性。
摘要：
ICD编码是将电子健康记录（EHRs）中的非结构化文本映射到由国际疾病分类系统（ICD）定义的标准代码的过程。为了促进信任和透明度，现有的ICD编码模型可解释性研究主要依赖于基于注意力的理由和由医生进行的定性评估，但缺乏在多种类型的理由上使用一致标准和高质量的理由标注数据集进行系统评估。此外，专门训练以生成合理理由的方法仍然很少。在本工作中，我们评估了ICD编码中理由的可解释性，重点在于两个基本维度：忠实性和合理性——即理由如何影响模型决策以及人类如何认为它们合理。在合理性方面，我们构建了一个新的、多粒度的理由标注ICD编码数据集，基于MIMIC-IV数据库和更新的ICD-10编码系统。我们对三种类型的ICD编码理由进行了全面评估：实体层面的自动构建通过实体链接、LLM生成的理由以及基于ICD编码模型注意力分数的理由。在LLM生成的理由表现出强合理性的基础上，我们进一步利用它们作为远距离监督信号来开发理由学习方法。此外，通过提示LLM使用我们数据集中的少量人工标注示例，我们实现了在教师LLM和学生理由学习模型中生成理由合理性的显著提升。
英文摘要：
ICD coding is the process of mapping unstructured text from Electronic Health Records (EHRs) to standardised codes defined by the International Classification of Diseases (ICD) system. In order to promote trust and transparency, existing explorations on the explainability of ICD coding models primarily rely on attention-based rationales and qualitative assessments conducted by physicians, yet lack a systematic evaluation across diverse types of rationales using consistent criteria and high-quality rationale-annotated datasets specifically designed for the ICD coding task. Moreover, dedicated methods explicitly trained to generate plausible rationales remain scarce. In this work, we present evaluations of the explainability of rationales in ICD coding, focusing on two fundamental dimensions: faithfulness and plausibility -- in short how rationales influence model decisions and how convincing humans find them. For plausibility, we construct a novel, multi-granular rationale-annotated ICD coding dataset, based on the MIMIC-IV database and the updated ICD-10 coding system. We conduct a comprehensive evaluation across three types of ICD coding rationales: entity-level mentions automatically constructed via entity linking, LLM-generated rationales, and rationales based on attention scores of ICD coding models. Building upon the strong plausibility exhibited by LLM-generated rationales, we further leverage them as distant supervision signals to develop rationale learning methods. Additionally, by prompting the LLM with few-shot human-annotated examples from our dataset, we achieve notable improvements in the plausibility of rationale generation in both the teacher LLM and the student rationale learning models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
