用疾病编码和医学知识图谱提升大模型生成临床病历的质量
Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
- 结合ICD编码、医学本体知识图谱与思维链提示
- 在6个病例上优于标准单次提示生成效果
- 适合医疗AI研发者和临床辅助系统开发者
过去十年，美国电子健康记录（EHR）数据激增，源于2009年《健康信息技术经济与临床健康法案》（HITECH Act）和2016年《21世纪治愈法案》的推动。医生需以自由文本形式录入患者评估、诊断和治疗的临床笔记，耗时较多，影响诊疗效率。大型语言模型（LLMs）具备生成类人新闻的能力。本文研究使用思维链（Chain-of-Thought, CoT）提示工程提升大模型在临床笔记生成中的表现。提示输入包含国际疾病分类（ICD）编码和基础患者信息，并融合传统CoT与语义搜索结果，进一步引入基于临床本体构建的知识图谱（KG），增强领域知识。我们在CodiEsp测试数据集的六个临床案例上使用GPT-4验证该方法，结果显示其生成的临床笔记优于标准单次提示生成结果。
原文摘要 · Abstract (English)
In the past decade a surge in the amount of electronic health record (EHR) data in the United States, attributed to a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes for patients' assessments, diagnoses, and treatments are captured in these EHRs in free-form text by physicians, who spend a considerable amount of time entering and editing them. Manually writing clinical notes takes a considerable amount of a doctor's valuable time, increasing the patient's waiting time and possibly delaying diagnoses. Large language models (LLMs) possess the ability to generate news articles that closely resemble human-written ones. We investigate the usage of Chain-of-Thought (CoT) prompt engineering to improve the LLM's response in clinical note generation. In our prompts, we use as input International Classification of Diseases (ICD) codes and basic patient information. We investigate a strategy that combines the traditional CoT with semantic search results to improve the quality of generated clinical notes. Additionally, we infuse a knowledge graph (KG) built from clinical ontology to further enrich the domain-specific knowledge of generated clinical notes. We test our prompting technique on six clinical cases from the CodiEsp test dataset using GPT-4 and our results show that it outperformed the clinical notes generated by standard one-shot prompts.
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