大型语言模型驱动的情绪分析,以促进纤维肌痛诊断
Vincenzo Venerito1, Florenzo Iannone2
1Rheumatology Unit - Department of Precision and Regenerative Medicine and Ionian Area, University of Bari "Aldo Moro", Bari, Italy.
RMD open
|June 28, 2024
概括
大型语言模型 (LLM) 通过分析疼痛表达细微差别,在诊断纤维肌痛 (FM) 中表现出前途. 快速设计的情绪分析显著提高了区分FM患者与对照者的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 纤维肌痛 (FM) 是一种复杂的疾病,其特点是广泛的疼痛和情绪困扰,带来诊断挑战.
- FM患者表现出改变的认知和情绪处理,注意力倾向于与疼痛相关的信息.
- 这种偏见可能会损害诸如抑制控制等认知功能,影响情绪管理和表达.
研究的目的:
- 调查开源大型语言模型 (LLM) 驱动的情绪分析在帮助纤维肌痛诊断中的实用性.
- 评估针对FM相关语言细微差别的特定提示工程是否提高了诊断准确性.
主要方法:
- 招募了40名纤维肌痛患者和40名慢性疼痛控制组.
- 使用Mistral-7B-Instruct-v0.2 LLM.分析了转录的患者反应.
- 采用了两种分析方法:针对FM细微差别进行快速工程和一个废弃 (非工程) 方法.
- 计算的诊断性能指标 (准确性,精度,回忆,特异性,AUROC) 与类风湿病学家诊断相比.
主要成果:
- 快速设计的LLM方法实现了高性能:准确度为0.87,精度为0.92,回忆率为0.84,特异性为0.82和AUROC为0.86.
- 废弃方法的结果较低:准确度为0.76,精度为0.75,回忆率为0.77和特异性为0.75.
- 提示工程方法在统计学上显示出更高的准确性 (McNemar的测试p<0.001).
结论:
- 由LLM驱动的情绪分析,特别是通过快速工程,显示了促进纤维肌痛诊断的潜力.
- 这种方法可以检测出FM的疼痛表达的微妙语言差异.
- 建议进一步验证,包括二次性纤维肌痛患者.
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