分层提示结合推理型大语言模型提取免疫检查点抑制剂相关心肌炎证据的初步研究
Hierarchical prompting with reasoning large language models for immune checkpoint inhibitor-associated myocarditis evidence extraction pilot study
免疫检查点抑制剂可引发心肌炎等严重免疫相关不良事件，但相关研究需要大量人工从病历中提取数据。研究者在单中心收集免疫检查点抑制剂相关心肌炎患者的临床记录，比较三种技术：大语言模型推理、上下文工程和分层提示。结果显示，基于句子的上下文工程结合分层提示和推理型模型，准确度显著高于研究人员（F1分数0.6332对0.4933），速度更快（每千份病历37分钟对72小时），成本更低（每千份2.04美元）。该流程有望帮助医生和研究者快速收集临床数据。
为什么推荐给您：把分层提示与推理型大语言模型用于特定不良事件证据提取，属方法学新探索，但为单中心小规模初步研究。
不需要生物学背景，多打比方
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摘要Abstract
AIMS: Immunotherapy with immune checkpoint inhibitors (ICIs) is an effective treatment for many cancers, but it can induce immune-related adverse events (irAEs). Among those, myocarditis is a severe complication associated with high morbidity and mortality. To support the rapidly emerging research, labour-intensive manual data extraction is often needed. Recent studies adopted large language models (LLMs) to systematically process clinical notes and identify patients with irAEs. However, research gaps persist: (i) For ICI-associated myocarditis, the applicability and scalability of LLMs have not been systematically evaluated; (ii) most existing studies focused on patient-level irAE detection while omitting the clinical nuances; and (iii) cutting-edge LLM techniques have not been adopted. Our aim was to evaluate an LLM approach to extract ICI-associated myocarditis evidence, including inflammatory infiltrate, life-threatening arrhythmias, heart failure, and stroke, from clinical notes.
METHODS AND RESULTS: We collected clinical notes of patients with ICI-associated myocarditis at a single centre to develop and evaluate LLM-based methods that convert free-text clinical notes into structured data with entities (e.g. diagnosis, treatment, and imaging) and attributes (e.g. date, assertion, and status). We systematically evaluated three techniques: LLM reasoning, context engineering, and hierarchical prompting against ground truth created by a cardio-oncologist. Our proposed sentence-based context engineering and hierarchical prompting method, with a reasoning LLM, is significantly more accurate than the research trainees (F1 score of 0.6332 vs. 0.4933, P < 0.001), faster (37 min vs. 72 h per 1000 notes), and more cost-effective ($2.04 USD per 1000 notes).
CONCLUSION: We proposed an optimized method that integrates hierarchical prompting, context engineering, and a reasoning LLM for ICI-associated myocarditis evidence extraction to support physicians and researchers in rapid clinical data collection for research to make diagnostic and treatment inferences.