利用ICD-10、临床本体知识图谱和链式推理提示法提升临床笔记生成
Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
AI总结：
本研究利用ICD-10、临床本体知识图谱和链式推理提示法提升临床笔记生成质量，通过GPT-4在六个临床案例中验证了方法的有效性。
摘要：
过去十年中，美国电子健康记录（EHR）数据量激增，这归因于2009年通过的《健康信息科技促进经济和临床健康法案》（HITECH法案）以及2016年通过的《21世纪治愈法案》。这些EHR中，医生以自由文本形式记录患者的评估、诊断和治疗，花费大量时间输入和编辑这些内容。手动撰写临床笔记耗费医生大量时间，增加患者等待时间并可能延误诊断。大型语言模型（LLMs）能够生成与人类写作相似的新闻文章。我们研究了使用链式推理（CoT）提示工程来改进LLM在临床笔记生成中的响应。在我们的提示中，我们使用国际疾病分类（ICD）代码和基本患者信息作为输入。我们研究了一种结合传统CoT与语义搜索结果的策略，以提高生成的临床笔记质量。此外，我们还引入了从临床本体构建的知识图谱（KG），以进一步丰富生成的临床笔记的领域特定知识。我们使用GPT-4在CodiEsp测试数据集的六个临床案例上测试我们的提示技术，结果表明其优于标准单次提示生成的临床笔记。
英文摘要：
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.
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。
