678 citations · 1.6k across the 35 of their papers we have counts for
25 papers · 1 filter
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets
Jintai Chen, Yaojun Hu, Mingchen Cai +13
Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning ove…
DeCom: Deep Coupled-Factorization Machine for Post COVID-19 Respiratory Syncytial Virus Prediction with Nonpharmaceutical Interventions Awareness
Xinyan Li, Cheng Qian, Lucas Glass
Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children. Due to the nonpharmaceutical intervention (NPI) imposed in the C…
Knowledge-Driven New Drug Recommendation
Zhenbang Wu, Huaxiu Yao, Zhe Su +5
Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervise…
Clinical trial site matching with improved diversity using fair policy learning
Rakshith S Srinivasa, Cheng Qian, Brandon Theodorou +4
The ongoing pandemic has highlighted the importance of reliable and efficient clinical trials in healthcare. Trial sites, where the trials are conducted, are chosen mainly based on…
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion
Cheng Qian, Kejun Huang, Lucas Glass +2
Tensor completion aims at imputing missing entries from a partially observed tensor. Existing tensor completion methods often assume either multi-linear or nonlinear relationships…
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Cheng Qian, Nikos Kargas, Cao Xiao +3
Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple d…
Change Matters: Medication Change Prediction with Recurrent Residual Networks
Chaoqi Yang, Cao Xiao, Lucas Glass +1
Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all m…
Machine Learning Applications for Therapeutic Tasks with Genomics Data
Kexin Huang, Cao Xiao, Lucas M. Glass +3
Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and devel…
SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations
Chaoqi Yang, Cao Xiao, Fenglong Ma +2
Medication recommendation is an essential task of AI for healthcare. Existing works focused on recommending drug combinations for patients with complex health conditions solely bas…
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Zhen Lin, Cao Xiao, Lucas Glass +2
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting. Recent works tried to cont…
PyHealth: A Python Library for Health Predictive Models
Yue Zhao, Zhi Qiao, Cao Xiao +2
Despite the explosion of interest in healthcare AI research, the reproducibility and benchmarking of those research works are often limited due to the lack of standard benchmark da…
STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization
Nikos Kargas, Cheng Qian, Nicholas D. Sidiropoulos +3
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to…
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
Chacha Chen, Junjie Liang, Fenglong Ma +3
Successful health risk prediction demands accuracy and reliability of the model. Existing predictive models mainly depend on mining electronic health records (EHR) with advanced de…
MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
Tianfan Fu, Cao Xiao, Xinhao Li +2
Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple drug properties while maintainin…
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Yue Yu, Kexin Huang, Chao Zhang +3
Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning…
COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching
Junyi Gao, Cao Xiao, Lucas M. Glass +1
Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic he…
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
Rakshith S Srinivasa, Cao Xiao, Lucas Glass +2
The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during b…
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Kexin Huang, Tianfan Fu, Lucas Glass +3
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However,…
CLARA: Clinical Report Auto-completion
Siddharth Biswal, Cao Xiao, Lucas M. Glass +2
Generating clinical reports from raw recordings such as X-rays and electroencephalogram (EEG) is an essential and routine task for doctors. However, it is often time-consuming to w…
StageNet: Stage-Aware Neural Networks for Health Risk Prediction
Junyi Gao, Cao Xiao, Yasha Wang +3
Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Netw…
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
Limeng Cui, Siddharth Biswal, Lucas M. Glass +3
Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and…
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment
Siddharth Biswal, Cao Xiao, Lucas M. Glass +2
Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor repr…
CASTER: Predicting Drug Interactions with Chemical Substructure Representation
Kexin Huang, Cao Xiao, Trong Nghia Hoang +2
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and soci…
Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model
Fan Zhang, Tong Wu, Yunlong Wang +5
Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the comm…