PVminerLLM: 从患者生成的文本中使用大语言模型进行结构化提取患者声音
PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models
AI总结：
PVminerLLM通过监督微调的大语言模型从患者生成的文本中提取结构化患者声音，实现高精度的F1分数，为健康公平性和护理协调提供新的分析工具。
摘要：
动机：患者生成的文本包含关于患者生活经历、社会状况和护理参与的重要信息，包括强烈影响依从性、护理协调和健康公平性的因素。然而，这些患者声音信号很少以结构化形式出现，限制了其在以患者为中心的结局研究和临床质量改进中的应用。因此，可靠地提取此类信息对于理解并解决健康结果的非临床驱动因素至关重要。\n\n结果：我们介绍了PVminer，一个用于结构化提取患者声音的基准，并提出了PVminerLLM，一种针对此任务进行监督微调的大型语言模型。在多个数据集和模型大小上，PVminerLLM显著优于基于提示的基线方法，实现了代码预测的最高83.82% F1，子代码预测的80.74% F1，以及证据跨度提取的87.03% F1。值得注意的是，即使使用较小的模型也能实现强劲的性能，这表明在不使用极端模型规模的情况下也能可靠地提取患者声音。这些结果使对嵌入在患者生成文本中的社会和体验信号进行可扩展分析成为可能。\n\n可用性与实现：代码、评估脚本和训练好的LLM将被公开发布。注释数据集将在请求后提供用于研究用途。\n\n关键词：大型语言模型，监督微调，医疗注释，患者生成文本，临床NLP
英文摘要：
Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available in structured form, limiting their use in patient-centered outcomes research and clinical quality improvement. Reliable extraction of such information is therefore essential for understanding and addressing non-clinical drivers of health outcomes at scale. Results: We introduce PVminer, a benchmark for structured extraction of patient voice, and propose PVminerLLM, a supervised fine-tuned large language model tailored to this task. Across multiple datasets and model sizes, PVminerLLM substantially outperforms prompt-based baselines, achieving up to 83.82% F1 for Code prediction, 80.74% F1 for Sub-code prediction, and 87.03% F1 for evidence Span extraction. Notably, strong performance is achieved even with smaller models, demonstrating that reliable patient voice extraction is feasible without extreme model scale. These results enable scalable analysis of social and experiential signals embedded in patient-generated text. Availability and Implementation: Code, evaluation scripts, and trained LLMs will be released publicly. Annotated datasets will be made available upon request for research use. Keywords: Large Language Models, Supervised Fine-Tuning, Medical Annotation, Patient-Generated Text, Clinical NLP
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。
