Global and Episode-Specific Prediction of Recurrent Events Using Longitudinal Health Informatics Data [0.03%]
基于健康信息纵向数据的复发事件全局和特异时相阶段预测
Yifei Sun,Sy Han Chiou,Chiung-Yu Huang
Yifei Sun
Accurate prediction of recurrent clinical events is crucial for effective management of chronic conditions such as cancer and cardiovascular disease. In recent years, longitudinal health informatics databases, which routinely collect data o...
Immune Profiling among Colorectal Cancer Subtypes using Dependent Mixture Models [0.03%]
依赖混合模型在结直肠癌亚型免疫谱分析中的应用
Yunshan Duan,Shuai Guo,Wenyi Wang et al.
Yunshan Duan et al.
Comparison of transcriptomic data across different conditions is of interest in many biomedical studies. In this paper, we consider comparative immune cell profiling for early-onset (EO) versus late-onset (LO) colorectal cancer (CRC). EOCRC...
Anna Neufeld,Ameer Dharamshi,Lucy L Gao et al.
Anna Neufeld et al.
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information [0.03%]
一种用于整合局部和全局信息的模型不可知图神经网络
Wenzhuo Zhou,Annie Qu,Keiland W Cooper et al.
Wenzhuo Zhou et al.
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to ...
Zigzag path connects two Monte Carlo samplers: Hamiltonian counterpart to a piecewise deterministic Markov process [0.03%]
之字形路径连接两个蒙特卡罗采样器：与分段确定性马尔可夫过程相对应的汉密尔顿方法
Akihiko Nishimura,Zhenyu Zhang,Marc A Suchard
Akihiko Nishimura
Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-...
Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens [0.03%]
控制学习最优动态治疗方案的累积不利风险
Mochuan Liu,Yuanjia Wang,Haoda Fu et al.
Mochuan Liu et al.
Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy b...
Estimating Higher-Order Mixed Memberships via the ℓ 2,∞ Tensor Perturbation Bound [0.03%]
基于ℓ２，无穷范数张量摄动界的高阶混合成员估计
Joshua Agterberg,Anru R Zhang
Joshua Agterberg
Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we ...
Neyman-Pearson Multi-class Classification via Cost-sensitive Learning [0.03%]
成本敏感学习下的 Neyman-Pearson 多分类方法
Ye Tian,Yang Feng
Ye Tian
Most existing classification methods aim to minimize the overall misclassification error rate. However, in applications such as loan default prediction, different types of errors can have varying consequences. To address this asymmetry issu...
Simeng Shao,Jacob Bien,Adel Javanmard
Simeng Shao
In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which in turn belong to ever coarser divisions suc...
Daniel R Kowal,Bohan Wu
Daniel R Kowal
Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations...