以注意力为基础的合成数据生成用于校准增强生存分析:使用电子健康记录进行慢性病病例研究
Nicholas I-Hsien Kuo1, Blanca Gallego1, Louisa Jorm1
1Centre for Big Data Research in Health (CBDRH), The University of New South Wales, Sydney 2033, New South Wales, Australia.
Journal of biomedical informatics
|November 8, 2025
概括
蒙面临床建模 (MCM) 生成合成生存数据,以改善临床预测模型的校准. 这种保护隐私的方法提高了跨不同患者子组的公平性和可靠性,而无需重新培训.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 由于隐私问题和数据不平衡,对现实世界医疗保健数据的访问受到限制,这阻碍了公平的临床预测模型的开发.
- 现有的合成数据生成方法往往难以维持模型校准,并使特定子组的数据增强.
研究的目的:
- 介绍面具临床建模 (MCM),这是一个基于注意力的框架,用于生成合成生存数据.
- 使用合成数据,提高临床预测模型在整体和分层分析中的校准.
主要方法:
- 在没有直接的生存目标训练的情况下,MCM采用掩盖特征重建来学习数据依赖性.
- 该框架支持独立数据集合成和条件增强,用于在不需要再培训的情况下针对性地生成子队列.
- 在慢性病 (CKD) 电子健康记录 (EHR) 数据集上评估,MCM与使用校准损失,Cox模型一致性和Kaplan-Meier忠度的八种基线方法进行了比较.
主要成果:
- 由MCM生成的数据准确地复制了真实数据集的统计属性,保留了危险比率,并匹配了时间到事件曲线.
- 在MCM增强数据上训练的模型显示了更好的校准,整体校准损失减少了15%,子组元校准损失减少了9%.
- 在高风险子组 (糖尿病,功能障碍,晚年) 中,改善一致,不需要重新培训或针对结果的调整.
结论:
- MCM提供了一个灵活的框架来生成合成生存数据,可以明显改善风险模型校准.
- 执行可重复数据集合成和条件子组增强的能力使MCM成为保护隐私,校准意识学习的宝贵工具.
- 这项研究强调了合成数据在超越隐私应用的临床建模中提高公平性和可靠性的潜力.
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