89 citations · 244 across the 29 of their papers we have counts for
32 papers
Search for Truth from Reasoning: A Dynamic Representation Editing Framework for Steering LLM Trajectories
Tianlong Wang, Yuhang Wang, Weibin Liao +5
Current approaches to enhance Large Language Model (LLM) reasoning, such as Chain-of-Thought and "Wait" prompts, primarily encourage models to think more, yet often fail to guide t…
GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models
Yue Fang, Weibin Liao, Yuxin Guo +8
Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare. While large language models (LLMs) offer a promising paradigm v…
Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics
Yinghao Zhu, Dehao Sui, Zixiang Wang +13
Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision…
Auditing medical multi-agent AI reveals risks of false consensus
Yinghao Zhu, Lei Gu, Zixiang Wang +11
Large language models are increasingly being assembled into medical multi-agent systems that emulate multidisciplinary consultation through specialist roles, peer review and consen…
Toward Better EHR Reasoning in LLMs: Reinforcement Learning with Expert Attention Guidance
Yue Fang, Yuxin Guo, Jiaran Gao +9
Improving large language models (LLMs) for electronic health record (EHR) reasoning is essential for enabling accurate and generalizable clinical predictions. While LLMs excel at m…
ConfAgents: A Conformal-Guided Multi-Agent Framework for Cost-Efficient Medical Diagnosis
Huiya Zhao, Yinghao Zhu, Zixiang Wang +3
The efficacy of AI agents in healthcare research is hindered by their reliance on static, predefined strategies. This creates a critical limitation: agents can become better tool-u…
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs
Hongxin Ding, Baixiang Huang, Yue Fang +8
Interactive medical questioning is essential in clinical consultations, where physicians must actively gather necessary patient information. Yet existing medical Large Language Mod…
Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation
Weibin Liao, Tianlong Wang, Yinghao Zhu +3
Medical Lay Language Generation (MLLG) plays a vital role in improving the accessibility of complex scientific content for broader audiences. Recent literature to MLLG commonly emp…
HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research
Yinghao Zhu, Yifan Qi, Zixiang Wang +8
The rapid proliferation of scientific knowledge presents a grand challenge: transforming this vast repository of information into an active engine for discovery, especially in high…
MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks
Yinghao Zhu, Ziyi He, Haoran Hu +6
The rapid advancement of Large Language Models (LLMs) has stimulated interest in multi-agent collaboration for addressing complex medical tasks. However, the practical advantages o…
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration
Zixiang Wang, Yinghao Zhu, Huiya Zhao +9
We introduce ColaCare, a framework that enhances Electronic Health Record (EHR) modeling through multi-agent collaboration driven by Large Language Models (LLMs). Our approach seam…
ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks
Yinghao Zhu, Junyi Gao, Zixiang Wang +9
Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility in non-generative clinical prediction, often presumed inferior to specialized models, rem…
EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation
Yinghao Zhu, Changyu Ren, Zixiang Wang +7
The integration of multimodal Electronic Health Records (EHR) data has significantly advanced clinical predictive capabilities. Existing models, which utilize clinical notes and mu…
Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories
Tianlong Wang, Xianfeng Jiao, Yinghao Zhu +6
Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false state…
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
Weibin Liao, Yinghao Zhu, Xinyuan Wang +3
UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their…
Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data
Yinghao Zhu, Zixiang Wang, Junyi Gao +6
The inherent complexity of structured longitudinal Electronic Health Records (EHR) data poses a significant challenge when integrated with Large Language Models (LLMs), which are t…
Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series
Zhihao Yu, Xu Chu, Liantao Ma +2
Irregularly sampled time series are ubiquitous, presenting significant challenges for analysis due to missing values. Despite existing methods address imputation, they predominantl…
Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction
Weibin Liao, Yinghao Zhu, Zhongji Zhang +5
Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values…
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting
Zhihao Yu, Liantao Ma, Yasha Wang +1
Deep learning algorithms, especially Transformer-based models, have achieved significant performance by capturing long-range dependencies and historical information. However, the p…
Predict and Interpret Health Risk using EHR through Typical Patients
Zhihao Yu, Chaohe Zhang, Yasha Wang +3
Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction.…
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets
Zhongji Zhang, Yuhang Wang, Yinghao Zhu +7
Emerging diseases present challenges in symptom recognition and timely clinical intervention due to limited available information. An effective prognostic model could assist physic…
PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations
Yinghao Zhu, Zixiang Wang, Long He +4
Electronic Health Records (EHRs) contain a wealth of patient data; however, the sparsity of EHRs data often presents significant challenges for predictive modeling. Conventional im…
MFair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method
Yinghao Zhu, Jingkun An, Enshen Zhou +8
In the data-driven artificial intelligence paradigm, models heavily rely on large amounts of training data. However, factors like sampling distribution imbalance can lead to issues…
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
Xinyu Ma, Xu Chu, Yasha Wang +4
Graph data augmentation has shown superiority in enhancing generalizability and robustness of GNNs in graph-level classifications. However, existing methods primarily focus on the…