自然语言处理为患有先天性心脏病的成年人构建多中心可计算的表型图书馆
medRxiv : the preprint server for health sciences
|September 2, 2025
概括
开发了分类器,以确定生物库数据中的成人先天性心脏病 (ACHD) 表型. 在8种表型中,有6种被高精度地分类,支持质量改进和数据群体的努力.
科学领域:
- 心脏病学
- 生物信息学
- 机器学习
背景情况:
- 成人先天性心脏病 (ACHD) 需要精确的表型化才能有效管理和研究.
- 生物库对于存储患者数据至关重要,但需要准确的变量群体.
研究的目的:
- 开发和验证多种ACHD表型的自动分类器.
- 通过机器学习实现有效的生物库变量.
主要方法:
- 在1492名ACHD患者的标记数据集上对8种表型进行了训练.
- 使用较大的未标记数据集 (15869名患者) 进行预训练和验证.
- 使用了三种不同的分类器架构,并使用F1分数和正预测值来评估性能.
主要成果:
- 在八种表型的标记数据中获得F1分数从0.66到1.
- 在未标记的数据上验证了6种表型,预测值为81.5%至100%.
- 鉴定出色和NYHA功能类由于变异性和观察者协议问题而具有挑战性的表型.
结论:
- 在8种ACHD表型中成功分类了6种,表现令人满意.
- 证明了基于自然语言处理 (NLP) 的分类器对 ACHD 类型的有用性.
- 开发的分类器适用于质量改进计划和填充ACHD注册表.
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