转移学习预测2型糖尿病与未配对的临床和遗传数据
YounSung Jung1, SeanKyo Han1, EunHee Kang1
1Department of Life Science, Handong Global University, Pohang, Republic of Korea.
Scientific reports
|July 29, 2025
概括
这项研究引入了一个新的AI框架,用于使用有限的多式联络数据预测2型糖尿病 (T2DM) 风险. 该方法提高了预测准确度,有助于早期诊断和T2DM的管理.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 内分泌学 在内分泌学.
背景情况:
- 韩国2型糖尿病 (T2DM) 的患病率不断上升.
- 未被诊断的T2DM病例构成了重大的公共卫生挑战.
- 现有的人工智能模型面临着稀缺的多模式数据和阶级不平衡的挑战.
研究的目的:
- 开发一种新的转移学习框架,以预测T2DM在五年内出现.
- 在T2DM预测模型中解决数据稀缺性和阶级失衡问题.
- 提高AI在T2DM风险评估中的准确性和适用性.
主要方法:
- 使用转移学习框架与两个韩国队列 (KoGES和SNUH).
- 利用未配对的临床和遗传数据与配对的数据一起.
- 实施了负权重二元交叉 (BCE) 损失和权重随机抽样器 (WRS) 来处理类失衡.
主要成果:
- 转移学习框架显著改善了T2DM预测性能.
- WRS和加权的ECB损失提高了平衡精度和AUC (测试AUC为0.8441).
- 通过将转移学习与中间数据融合相结合,尽管对联数据有限,但实现了卓越的性能 (测试AUC 0.8715).
结论:
- 拟议的框架有效地管理T2DM预测的稀缺配对数据和类不平衡.
- 这种方法为早期T2DM诊断和改善疾病管理提供了有希望的策略.
- 该方法可适应其他医学预测任务,并可以整合额外的数据模式.
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