在全国纵向健康查数据上使用深度学习进行肝癌风险分层:一项回顾性队列研究
Yewon Choi1,2, Sungmin Cho1, Changdai Gu1,3
1Institute for Innovation in Digital Healthcare, Yonsei University Health System, Seoul, 03722, South Korea.
BMC medical informatics and decision making
|January 17, 2026
概括
一个新的深度学习模型使用国家健康查数据准确预测肝癌风险,早期识别高风险个体. 这种人工智能方法增强了查策略,以获得更好的公共卫生结果.
科学领域:
- 人工智能在医学中的应用
- 公共卫生监督 公共卫生监督
- 在瘤学瘤学.
背景情况:
- 目前的韩国肝癌查主要针对病毒性肝炎和肝硬化.
- 与代谢和酒精相关疾病相关的肝癌发病率不断上升,需要改进查.
- 需要风险预测模型,利用现有的医疗保健数据,而不需要额外的诊断测试.
研究的目的:
- 开发和验证一种深度学习模型,用于预测5年肝癌风险.
- 为了利用常规收集的国家查和索赔数据来分层肝癌风险.
- 将模型的性能与当前的国家监督标准进行比较.
主要方法:
- 一项追溯的队列研究,对3,962,209名年龄在50-69岁的成年人进行,使用韩国国家健康查数据 (2010-2015).
- 使用人口,临床,行为,人体和实验室特征开发一维卷积神经网络模型.
- 使用接收器操作特征曲线 (AUROC) 下的区域,灵敏度,特异性以及与后勤回归,极端梯度增强和多层感知器进行比较的性能评估.
主要成果:
- 深度学习模型实现了0.810的AUROC,明显超过了国家标准 (AUROC 0.552).
- 该模型在识别高风险个体方面表现出高灵敏度 (0.736).
- 风险最高的五分之一确定了65%的发生性肝癌病例,与风险最低的组相比,风险比率高27倍.
结论:
- 对国家查数据应用的深度学习有效地分层了肝癌风险,使得早期检测成为可能.
- 该模型识别了高风险个体,包括那些没有先前存在的肝病的人.
- 这种方法提供了一个可扩展的,与政策相关的战略,用于在现有医疗保健基础设施内加强癌症查.
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