Identifying Heterogeneous Effect using Latent Supervised Clustering with Adaptive Fusion.
Identifying Heterogeneous Effect using Latent Supervised Clustering with Adaptive Fusion.
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DOI：
10.1080/10618600.2020.1763808
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Liu Y
中科院分区：
文献类型：
--
作者：
Chen J;Tran-Dinh Q;Kosorok MR;Liu Y
精准医学是一个重要的研究领域，其目标是为每个患者确定最佳治疗方案。在文献中，提出了各种方法来划分群体的亚组根据个体的异质性效应。本文提出了一种新的探索性机器学习工具，名为潜在监督聚类，以识别异质子种群。特别是，我们制定的问题作为一个回归问题与主题特定的系数，并使用自适应融合聚类的系数成子群。这种方法有两个主要优点。首先，它依赖于小的先验知识和弱参数假设的基本亚种群结构。第二，它利用了结果-预测关系，因此可以具有竞争力的估计和预测精度。为了估计参数，我们设计了一个高效的加速近似梯度算法，保证收敛速度有竞争力。数值研究表明，该方法具有竞争力的估计和预测精度，也可以产生可解释的聚类结果的潜在的异质效应。
Precision medicine is an important area of research with the goal of identifying the optimal treatment for each individual patient. In the literature, various methods are proposed to divide the population into subgroups according to the heterogeneous effects of individuals. In this paper, a new exploratory machine learning tool, named latent supervised clustering, is proposed to identify the heterogeneous subpopulations. In particular, we formulate the problem as a regression problem with subject specific coefficients, and use adaptive fusion to cluster the coefficients into subpopulations. This method has two main advantages. First, it relies on little prior knowledge and weak parametric assumptions on the underlying subpopulation structure. Second, it makes use of the outcome-predictor relationship, and hence can have competitive estimation and prediction accuracy. To estimate the parameters, we design a highly efficient accelerated proximal gradient algorithm which guarantees convergence at a competitive rate. Numerical studies show that the proposed method has competitive estimation and prediction accuracy, and can also produce interpretable clustering results for the underlying heterogeneous effect.
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DOI：
10.1080/10618600.2014.948181
发表时间：
2015
期刊：
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
影响因子：
--
作者：
Chi EC;Lange K
通讯作者：
Lange K
DOI：
10.1080/01621459.2013.789695
发表时间：
2013-07-01
影响因子：
3.7
作者：
Wei S;Kosorok MR
通讯作者：
Kosorok MR
影响因子：
4.5
作者：
Zhao J;Yu G;Liu Y
通讯作者：
Liu Y
影响因子：
2.1
作者：
Beck, Amir;Teboulle, Marc
通讯作者：
Teboulle, Marc
DOI：
10.1080/01621459.2016.1148039
发表时间：
2017-01-01
影响因子：
3.7
作者：
Ma, Shujie;Huang, Jian
通讯作者：
Huang, Jian