人工智能用于检测电子医疗记录中的过敏反应
Luis Felipe Ensina1, Matheus Matos Machado2, Joice B Machado Marques3
1Department of Allergy and Clinical Immunology, Hospital Sírio-Libanês, São Paulo, Brazil.
Asia Pacific allergy
|September 11, 2025
概括
人工智能,特别是大型语言模型 (LLM),在从电子医疗记录中自动诊断过敏反应方面表现出高准确性. 这项技术可以通过确保及时正确识别过敏反应,显著提高患者的安全性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 过敏和免疫学 过敏和免疫学
背景情况:
- 由于临床复杂性和电子病历 (EMR) 中的记录不足,诊断过敏反应具有挑战性.
- 不准确或延迟诊断会增加复发,潜在的致命过敏反应的风险.
- 自动化系统,如大型语言模型 (LLM),为提高诊断准确性和患者安全提供了有希望的解决方案.
研究的目的:
- 从EMR文本中评估LLM在自主识别过敏反应诊断方面的有效性.
- 通过人工智能驱动的诊断支持,增强患者安全并优化护理服务.
- 为了比较不同LLM配置在检测过敏反应的诊断性能.
主要方法:
- 在LLMs (GPT 3.5,4和4 Turbo) 分析了969个巴西葡萄牙语医学文本.
- 文本被专家医生标注为过敏反应阳性或阴性.
- 用精度,灵敏度,特异性和准确度指标,LLM诊断建议与医生诊断进行了比较.
主要成果:
- 使用主提示符,GPT 4 Turbo实现了90.6%的精度,100%的灵敏度,99.5%的特异性和99.5%的准确性.
- 纳入世界过敏组织 (WAO) 标准略有改善了旧的LLM模型,但没有提高GPT 4 Turbo的精度.
- 该LLM的Cohen kappa系数高达0.95,表明与专家诊断有很强的一致性.
结论:
- 从EMR数据来看,LLM显示出自动化过敏反应诊断的巨大潜力.
- 由人工智能驱动的工具可以有效地支持医疗保健专业人员识别关键过敏反应.
- 自动诊断可以提高患者的安全性和更有效的医疗保健服务.
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