用大模型提取病历中的OPQRST信息，让AI像医生一样思考。
Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning
- 将提取任务转为文本生成，让模型模拟医生推理过程。
- 结合BERT Score等语义相似度指标，提升评估准确性。
- 适合临床信息提取、AI辅助诊断场景，提升可解释性。
电子健康记录（EHR）中关键患者信息的提取因数据复杂且非结构化而面临重大挑战。传统机器学习方法难以高效捕捉相关信息，限制了临床应用。本文提出一种新方法，利用大语言模型（LLM）从EHR中提取OPQRST评估内容。将任务从序列标注重构为文本生成，使模型能生成类医师认知的推理步骤，增强可解释性，并适应医疗数据标注稀缺的现实。针对机器生成文本在临床场景下的评估难题，我们改进了传统的命名实体识别（NER）指标，引入如BERT Score等语义相似度度量，以评估生成内容与原始记录临床意图的一致性。实验表明，该方法显著提升了信息提取的准确性和实用性，为临床决策提供更可靠支持，推动AI在医疗领域的落地应用。
原文摘要 · Abstract (English)
The extraction of critical patient information from Electronic Health Records (EHRs) poses significant challenges due to the complexity and unstructured nature of the data. Traditional machine learning approaches often fail to capture pertinent details efficiently, making it difficult for clinicians to utilize these tools effectively in patient care. This paper introduces a novel approach to extracting the OPQRST assessment from EHRs by leveraging the capabilities of Large Language Models (LLMs). We propose to reframe the task from sequence labeling to text generation, enabling the models to provide reasoning steps that mimic a physician's cognitive processes. This approach enhances interpretability and adapts to the limited availability of labeled data in healthcare settings. Furthermore, we address the challenge of evaluating the accuracy of machine-generated text in clinical contexts by proposing a modification to traditional Named Entity Recognition (NER) metrics. This includes the integration of semantic similarity measures, such as the BERT Score, to assess the alignment between generated text and the clinical intent of the original records. Our contributions demonstrate a significant advancement in the use of AI in healthcare, offering a scalable solution that improves the accuracy and usability of information extraction from EHRs, thereby aiding clinicians in making more informed decisions and enhancing patient care outcomes.
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