382 citations · 563 across the 24 of their papers we have counts for
11 papers · 1 filter
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Yikuan Li, Chengsheng Mao, Kaixuan Huang +4
Scarcity of health care resources could result in the unavoidable consequence of rationing. For example, ventilators are often limited in supply, especially during public health em…
Distribution Preserving Graph Representation Learning
Chengsheng Mao, Yuan Luo
Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted gr…
Constrained tensor factorization for computational phenotyping and mortality prediction in patients with cancer
Francisco Y Cai, Chengsheng Mao, Yuan Luo
Background: The increasing adoption of electronic health records (EHR) across the US has created troves of computable data, to which machine learning methods have been applied to e…
Natural language processing to identify lupus nephritis phenotype in electronic health records
Yu Deng, Jennifer A. Pacheco, Anh Chung +14
Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one…
Towards Expressive Graph Representation
Chengsheng Mao, Liang Yao, Yuan Luo
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most exist…
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
Chengsheng Mao, Liang Yao, Yuan Luo
Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automaticall…
Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model
Chengsheng Mao, Lijuan Lu, Bin Hu
In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distri…
Local Distribution in Neighborhood for Classification
Chengsheng Mao, Bin Hu, Lei Chen +2
The k-nearest-neighbor method performs classification tasks for a query sample based on the information contained in its neighborhood. Previous studies into the k-nearest-neighbor…
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
Yikuan Li, Liang Yao, Chengsheng Mao +3
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will…
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
Guoqing Chao, Chengsheng Mao, Fei Wang +2
ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpr…
Distribution Networks for Open Set Learning
Chengsheng Mao, Liang Yao, Yuan Luo
In open set learning, a model must be able to generalize to novel classes when it encounters a sample that does not belong to any of the classes it has seen before. Open set learni…