226 citations · 236 across the 9 of their papers we have counts for
9 papers
Extracting Post-Acute Sequelae of SARS-CoV-2 Infection Symptoms from Clinical Notes via Hybrid Natural Language Processing
Zilong Bai, Zihan Xu, Cong Sun +13
Accurately and efficiently diagnosing Post-Acute Sequelae of COVID-19 (PASC) remains challenging due to its myriad symptoms that evolve over long- and variable-time intervals. To a…
Federated Causal Inference in Healthcare: Methods, Challenges, and Applications
Haoyang Li, Jie Xu, Kyra Gan +2
Federated causal inference enables multi-site treatment effect estimation without sharing individual-level data, offering a privacy-preserving solution for real-world evidence gene…
Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications
David Oniani, Jordan Hilsman, Chengxi Zang +4
A drug molecule is a substance that changes the organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for tre…
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
Chengxi Zang, Fei Wang
Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised con…
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki +13
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major is…
Visualizing Deep Graph Generative Models for Drug Discovery
Karan Yang, Chengxi Zang, Fei Wang
Drug discovery aims at designing novel molecules with specific desired properties for clinical trials. Over past decades, drug discovery and development have been a costly and time…
MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Chengxi Zang, Fei Wang
Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process. Such graph g…
Neural Dynamics on Complex Networks
Chengxi Zang, Fei Wang
Learning continuous-time dynamics on complex networks is crucial for understanding, predicting and controlling complex systems in science and engineering. However, this task is ver…
Structural patterns of information cascades and their implications for dynamics and semantics
Chengxi Zang, Peng Cui, Chaoming Song +2
Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although the dynamics and se…