3 citations · 7 across the 12 of their papers we have counts for
5 papers · 1 filter
A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data
Seungyeon Lee, Ruoqi Liu, Feixiong Cheng +1
Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Most existing drug repurposing stud…
KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs
Ruoqi Liu, Lingfei Wu, Ping Zhang
Treatment effect estimation (TEE) is the task of determining the impact of various treatments on patient outcomes. Current TEE methods fall short due to reliance on limited labeled…
Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder
Seungyeon Lee, Ruoqi Liu, Wenyu Song +1
Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups…
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification
Seungyeon Lee, Ruoqi Liu, Wenyu Song +2
Precise estimation of treatment effects is crucial for evaluating intervention effectiveness. While deep learning models have exhibited promising performance in learning counterfac…
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes
Changchang Yin, Ruoqi Liu, Jeffrey Caterino +1
Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment re…