3 citations · 7 across the 12 of their papers we have counts for
12 papers
PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments
Ruoqi Liu, Imran Q. Mohiuddin, Austin J. Schoeffler +10
We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record (EHR) environments. Existing…
Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics
Jiayuan Chen, Ruoqi Liu, Zishan Gu +1
Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scarce. Existing multimodal approa…
ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access
Jiwoo Park, Ruoqi Liu, Avani Jagdale +5
We present ClinicalTrialsHub, an interactive search-focused platform that consolidates all data from ClinicalTrials.gov and augments it by automatically extracting and structuring…
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…
Teach Multimodal LLMs to Comprehend Electrocardiographic Images
Ruoqi Liu, Yuelin Bai, Xiang Yue +1
The electrocardiogram (ECG) is an essential non-invasive diagnostic tool for assessing cardiac conditions. Existing automatic interpretation methods suffer from limited generalizab…
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…
Estimating Individual Treatment Effects with Time-Varying Confounders
Ruoqi Liu, Changchang Yin, Ping Zhang
Estimating the individual treatment effect (ITE) from observational data is meaningful and practical in healthcare. Existing work mainly relies on the strong ignorability assumptio…
Clinical connectivity map for drug repurposing: using laboratory tests to bridge drugs and diseases
Qianlong Wen, Ruoqi Liu, Ping Zhang
Drug repurposing has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on p…
When deep learning meets causal inference: a computational framework for drug repurposing from real-world data
Ruoqi Liu, Lai Wei, Ping Zhang
Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Existing methods for drug repur…