一个新的混合普通学习模型与医疗保健应用程序
Lujia Wang1, Hairong Wang1, Yi Su2
1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society
|September 19, 2025
概括
本研究介绍了一种混合顺序学习器 (HOL),可以有效地训练机器学习模型,使用精确和间隔标签,这对于医疗保健应用至关重要. 霍尔在预测疾病进展,包括阿尔茨海默病方面表现出卓越的表现,优于现有方法.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 平凡的学习习惯.
背景情况:
- 普通学习 (OL) 模型在医疗保健中对于疾病分级和进展预测等任务是有价值的.
- 当精确的标签稀缺,但不精确的间隔标签丰富时,训练OL模型具有挑战性.
- 现有的关于OL的研究与不精确/间隔标签是有限的.
研究的目的:
- 开发一种用于顺序学习的新方法,该方法集成精确和间隔标记数据.
- 为应对有限的精确标记样本的常见医疗保健数据挑战.
- 在实际应用中提高顺序学习模型的稳定性和准确性.
主要方法:
- 提出一种混合正则学习者 (HOL) 模型,能够处理混合精确和间隔标签.
- 为HOL配方开发一个高效的优化算法.
- 在基准数据集上对最先进的OL方法进行HOL评估.
主要成果:
- 在四个基准测试数据集上,HOL显著优于现有的顺序学习方法.
- 拟议的HOL模型在使用多模式数据预测阿尔茨海默氏症 (AD) 进展速度方面取得了高准确性.
- 与现有方法相比,HOL在现实世界预测AD进展方面表现优越.
结论:
- 混合顺序学习器 (HOL) 为顺序学习提供了一个强大的解决方案,具有有限的精确标签和丰富的间隔标签.
- 霍尔显示出促进医疗保健应用的巨大潜力,特别是在预测阿尔茨海默病等疾病进展方面.
- 准确预测AD的进展速度可以帮助开发个性化干预策略,以适应轻度认知障碍 (MCI) 患者.
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