提高基于半监督学习方法的认知障碍高风险的识别性能
Sumei Yao1, Yan Zhang2, Jing Chen3
1Center for Studies of Information Resources, Wuhan University, Wuhan, China; School of Information Management, Wuhan University, Wuhan, China; Big Data Institute, Wuhan University, Wuhan, China.
Journal of biomedical informatics
|July 21, 2024
概括
一个新的半监督学习算法 (SS-PP) 有效地使用未标记的数据预测高风险的认知障碍 (HR-CI). 这种方法提高了模型效率,并为认知疾病提供了具有成本效益的医疗保健策略.
科学领域:
- 计算神经科学是一种计算神经科学.
- 机器学习在医疗保健中的应用
- 认知障碍的诊断 认知障碍的诊断
背景情况:
- 认知评估对于早期检测认知障碍至关重要,包括阿尔茨海默氏症和莱维体痴呆症.
- 目前的大规模查依赖于认知尺度,这可能是不敏感的或昂贵的.
- 在查中,用于认知功能评估的机器学习应用尚未得到充分探索,往往需要专家的注释.
研究的目的:
- 引入一个半监督学习算法,伪标签与回放 (SS-PP),以提高模型的效率.
- 通过利用未标记的数据,提高对认知障碍 (HR-CI) 高风险的预测.
- 探索认知医疗保健领域的成本有效策略.
主要方法:
- 开发并评估了一种半监督分类算法 (SS-PP),使用了 189 个标记和 215,078 个未标记的真实世界样本.
- 与14种传统的监督机器学习方法和其他先进的半监督算法进行SS-PP性能比较.
- 在预测模型中利用了体检数据.
主要成果:
- 基于梯度提升决策树 (GBDT) 的最佳SS-PP模型,实现了0.947.7的曲线下的面积 (AUC).
- 与监督学习模型相比,AUC平均提高了8%.
- 在评估的半监督方法中实现了最先进的性能.
结论:
- 通过半监督学习,率先使用有限的标记数据来预测HR-CI.
- 强调了将体检数据纳入体检数据的好处,以改善认知障碍风险预测.
- 对开发具有成本效益的认知疾病查和管理策略的影响.
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