SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification [0.03%]
基于子群识别的治疗效应估计方法SubgroupTE
Seungyeon Lee,Ruoqi Liu,Wenyu Song et al.
Seungyeon Lee et al.
Precise estimation of treatment effects is crucial for accurately evaluating the intervention. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE),...
HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation [0.03%]
HydraGAN：一种用于多目标数据生成的协作智能体模型
Chance Desmet,Diane J Cook
Chance Desmet
Generative adversarial networks have become a de facto approach to generate synthetic data points that resemble their real counterparts. We tackle the situation where the realism of individual samples is not the sole criterion for synthetic...
Indirectly-Supervised Anomaly Detection of Clinically-Meaningful Health Events from Smart Home Data [0.03%]
基于智能家庭数据的临床有意义健康事件的间接监督异常检测
Jessamyn Dahmen,Diane J Cook
Jessamyn Dahmen
Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditi...
Garrett Wilson,Diane J Cook
Garrett Wilson
Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not al...
Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network [0.03%]
一种基于主题的健康社会网络中时间动态的身体活动传播方法
Nhathai Phan,Javid Ebrahimi,David Kil et al.
Nhathai Phan et al.
Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and to helping spread wellness and healthy behavior in a social network. However, there have no...
Jure Leskovec,Rok Sosič
Jure Leskovec
Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipula...
Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR) [0.03%]
基于特征重映射的跨领域迁移学习方法
Kyle D Feuz,Diane J Cook
Kyle D Feuz
Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature- Space Remapping (FSR), which tr...
Achla Marathe,Zhengzheng Pan,Andrea Apolloni
Achla Marathe
We employ Add Health data to show that friendship networks, constructed from mutual friendship nominations, are important in building weight perception, setting weight goals and measuring social marginalization among adolescents and young a...
A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data [0.03%]
一种用于分类电子健康记录数据的时序模式挖掘方法
Iyad Batal,Hamed Valizadegan,Gregory F Cooper et al.
Iyad Batal et al.
We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal a...
Intelligent Systems and Technology for Integrative and Predictive Medicine: An ACP Approach [0.03%]
智能系统和集成预测医学技术：ACP方法
Fei-Yue Wang,Pak Kin Wong
Fei-Yue Wang
One of the principal goals in medicine is to determine and implement the best treatment for patients through fastidious estimation of the effects and benefits of therapeutic procedures. The inherent complexities of physiological and patholo...