用于高维风险预测的替代物辅助半监督推理。

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2023-01-01
Jue Hou, Zijian Guo, Tianxi Cai
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引用次数: 0

摘要

由于无法直接观察疾病结果和高维预测因子,利用电子健康记录(EHR)数据进行风险建模具有挑战性。在本文中,我们开发了一种代用数据辅助的半监督学习方法,该方法利用了带有注释结果的小标签数据以及大量未标签的结果代用数据和高维预测因子。我们建议通过利用结果代理和高维预测因子构建稀疏估算模型来估算未观察到的结果。我们还进一步进行了一步纠偏,以实现风险预测的区间估计。即使估算模型和风险预测模型都被错误地指定,我们的推断程序也是有效的。我们采用新颖的方法来充分利用未标注数据,从而能够在具有高密度风险预测模型的挑战性环境中进行高维统计推断。我们进行了广泛的模拟研究,以证明我们的方法与现有的监督方法相比具有优越性。我们利用电子病历生物库队列将该方法应用于 2 型糖尿病遗传风险预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction.

Risk modeling with electronic health records (EHR) data is challenging due to no direct observations of the disease outcome and the high-dimensional predictors. In this paper, we develop a surrogate assisted semi-supervised learning approach, leveraging small labeled data with annotated outcomes and extensive unlabeled data of outcome surrogates and high-dimensional predictors. We propose to impute the unobserved outcomes by constructing a sparse imputation model with outcome surrogates and high-dimensional predictors. We further conduct a one-step bias correction to enable interval estimation for the risk prediction. Our inference procedure is valid even if both the imputation and risk prediction models are misspecified. Our novel way of ultilizing unlabelled data enables the high-dimensional statistical inference for the challenging setting with a dense risk prediction model. We present an extensive simulation study to demonstrate the superiority of our approach compared to existing supervised methods. We apply the method to genetic risk prediction of type-2 diabetes mellitus using an EHR biobank cohort.

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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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