250 citations · 590 across the 14 of their papers we have counts for
7 papers · 1 filter
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
Pulakesh Upadhyaya, Kai Zhang, Can Li +2
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care. This p…
A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy
Kai Zhang, Chao Tian, Kun Zhang +2
The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data. It can be computationally expensive in the worst case due to the conditiona…
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark
Yaobin Ling, Pulakesh Upadhyaya, Luyao Chen +2
Developing new drugs for target diseases is a time-consuming and expensive task, drug repurposing has become a popular topic in the drug development field. As much health claim dat…
Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network
Kai Zhang, Siddharth Karanth, Bela Patel +2
We propose a robust in-time predictor for in-hospital COVID-19 patient's probability of requiring mechanical ventilation. A challenge in the risk prediction for COVID-19 patients l…
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
Yuqi Si, Jingcheng Du, Zhao Li +5
Patient representation learning refers to learning a dense mathematical representation of a patient that encodes meaningful information from Electronic Health Records (EHRs). This…
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…
Federated Tensor Factorization for Computational Phenotyping
Yejin Kim, Jimeng Sun, Hwanjo Yu +1
Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models…