III: Small: Collaborative Research: Comprehensive Heterogeneous Response Regression from Complex Data

III：小：协作研究：复杂数据的综合异质响应回归

• 批准号: 1716432
• 负责人: Fei Wang
• 金额: $ 24.9万
• 依托单位: Joan and Sanford I. Weill Medical College of Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1716432&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Comprehensive

Predictive modeling is one fundamental problem in supervised machine learning. Traditional predictive modeling approaches typically built one predictor for each prediction task. However, in many real world problems, one needs to build predictors for multiple inter-correlated tasks simultaneously. For example, in real-time anesthesia decision making, the anesthesia drugs will have impact on multiple indicators of an anesthesia patient, such as anesthesia depth, blood pressures, heart rates, etc. The anesthesiologist needs to consider all those different aspects as well as their intrinsic dependence before s/he can make the decision. The goal of this project is to conduct systematic research on heterogeneous response regression, which builds multiple regression models for heterogeneous responses as well as exploring the relationship among them.Specifically, the project's heterogeneous response regression framework is based on a tailored latent factor model that captures the relationship among different responses in a low-dimensional space. The response heterogeneity will be captured by utilizing the exponential family distributions and beyond. The model parameters can be learned through regularized maximum likelihood estimation via a majorization-minimization procedure. Moreover, a distributed solution based on the alternating direction method of multipliers can be derived to enhance the scalability. The project will also make the framework more flexible by (1) adding individualized effect terms to capture sample/feature heterogeneity and induce task dependency; and (2) leveraging a functional model to account for time-varying predictors and to make the prediction time-sensitive. The project will demonstrate the effectiveness of its method in disease risk prediction and financial stability modeling.

预测建模是有监督机器学习中的一个基本问题。传统的预测建模方法通常为每个预测任务构建一个预测器。然而，在许多现实世界的问题中，人们需要同时为多个相互关联的任务建立预测器。例如，在实时麻醉决策中，麻醉药物会对麻醉患者的多项指标产生影响，如麻醉深度、血压、心率等。麻醉医生需要考虑所有这些不同方面以及它们之间的内在相关性，才能做出决定。该项目的目标是对异质响应回归进行系统的研究，建立异质响应的多元回归模型，并探索它们之间的关系。具体而言，该项目的异质响应回归框架基于一个定制的潜在因素模型，该模型捕捉了低维空间中不同响应之间的关系。响应的异质性将通过利用指数族分布和更高的分布来捕捉。模型参数可以通过优化-最小化过程通过正则化最大似然估计来学习。此外，还可以推导出基于乘子交替方向方法的分布式解决方案，以增强可伸缩性。该项目还将通过以下方式使框架更加灵活：(1)增加个性化的影响项，以捕捉样本/特征的异质性并诱导任务依赖；以及(2)利用功能模型来考虑时变预测因素，并使预测具有时间敏感性。该项目将展示其方法在疾病风险预测和金融稳定性建模方面的有效性。

项目成果

Uncovering Pattern Formation of Information Flow

• DOI: 10.1145/3292500.3330971
• 发表时间: 2019-07
• 期刊: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Chengxi Zang;Peng Cui;Chaoming Song;Wenwu Zhu;Fei Wang

Recent Advances on Graph Analytics and Its Applications in Healthcare

• DOI: 10.1145/3394486.3406469
• 发表时间: 2020-07
• 期刊: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Fei Wang;Peng Cui;J. Pei;Yangqiu Song;Chengxi Zang

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning

• DOI: 10.1093/clinchem/hvaa200
• 发表时间: 2020-11-01
• 期刊: CLINICAL CHEMISTRY
• 影响因子: 9.3
• 作者: Yang, He S.;Hou, Yu;Wang, Fei
• 通讯作者: Wang, Fei

Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders

• DOI: 10.24963/ijcai.2018/483
• 发表时间: 2018-04
• 作者: Tengfei Ma;Cao Xiao;Jiayu Zhou;Fei Wang

Structural Deep Embedding for Hyper-Networks

• DOI: 10.1609/aaai.v32i1.11266
• 发表时间: 2017-11
• 期刊: ArXiv
• 作者: Ke Tu;Peng Cui;Xiao Wang;Fei Wang;Wenwu Zhu