对心力衰竭的语言模型的多标准优化,在西班牙的电子健康记录中检测出保存的喷射分数症状:比较建模研究
Jacinto Mata1, Victoria Pachón1, Ana Manovel2
1I²C Research Group, Universidad de Huelva, Huelva, 21007, Spain, +34 687862089.
Journal of medical Internet research
|July 17, 2025
概括
这项研究开发了基于变压器的自然语言处理模型,用于在西班牙电子健康记录中检测心力衰竭与保存弹射率 (HFpEF) 症状. 这些模型显示出高准确度和灵敏度,有助于早期诊断心脏粉样性粉症.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能在医学中的应用
- 临床信息学 临床信息学
背景情况:
- 保存喷射分数 (HFpEF) 的心力衰竭是心肌粉症的关键指标,由于模糊的症状,往往被诊断不足.
- 电子健康记录 (EHR) 通过NLP提供了早期症状检测的潜力,但在西班牙的电子健康记录中存在挑战.
- 缺乏注释的体和特定领域的模型阻碍了在西班牙临床叙述中NLP的应用.
研究的目的:
- 评估NLP的可行性,以识别HFpEF表型在西班牙EHR的早期心脏粉症检测.
- 评估特定领域的语言模型和优化策略对症状检测可靠性,敏感性和通用性的影响.
- 通过西班牙语医疗保健机构的先进NLP技术,改善心脏粉症的早期诊断.
主要方法:
- 开发并验证了由心脏病专家注释的15,304份西班牙临床文档的新型集体.
- 评估了8个基于变压器的语言模型,包括一般和生物医学专业的变体 (例如,Longformer).
- 在未见患者数据的微调过程中应用了3种临床指导优化策略 (AUC,F1得分,灵敏度).
主要成果:
- 所有评估的模型都表现出高性能,AUC>0.940.
- 顶级模型,Longformer生物医学临床,实现AUC为0.987,F1得分为0.985,灵敏度为0.987,特异性为0.987.
- 灵敏度优化将假阴性率降低到3%以下,这对临床安全至关重要.
结论:
- 变压器模型可靠地检测西班牙电子健康系统中的HFpEF症状,克服了阶级不平衡和语言复杂性.
- 特定领域的预训练,长文本架构和临床优化提高了分类性能,特别是灵敏度.
- 这些模型显示了作为有效的查工具的潜力,以优先考虑在西班牙语地区患心脏粉症风险的患者.
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