250 citations · 590 across the 14 of their papers we have counts for
14 papers
Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang +1
Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance i…
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…
An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles
Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz +10
Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multip…
Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules
Yan Ding, Xiaoqian Jiang, Yejin Kim
Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in…
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…
Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
Kanglin Hsieh, Yinyin Wang, Luyao Chen +5
Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but t…
From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort
Rui Zhang, Luca Giancardo, Danilo A. Pena +3
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD). Using a simple abstraction techni…
Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease
Xiaoqian Jiang, Samden Lhatoo, Guo-Qiang Zhang +2
Existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without. The goal…
Discriminative Sleep Patterns of Alzheimer's Disease via Tensor Factorization
Yejin Kim, Xiaoqian Jiang, Luyao Chen +2
Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages. Although sleep disturbance is…
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…