89 citations · 244 across the 29 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset
Liantao Ma, Chaohe Zhang, Junyi Gao +8
Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identif…
MCare: Learning with Missing Modalities in Multimodal Healthcare Data
Chaohe Zhang, Xu Chu, Liantao Ma +4
Multimodal electronic health record (EHR) data are widely used in clinical applications. Conventional methods usually assume that each sample (patient) is associated with the unifi…
A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care
Junyi Gao, Yinghao Zhu, Wenqing Wang +4
The COVID-19 pandemic has posed a heavy burden to the healthcare system worldwide and caused huge social disruption and economic loss. Many deep learning models have been proposed…
MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering
Xinyu Ma, Xu Chu, Yasha Wang +4
In healthcare prediction tasks, it is essential to exploit the correlations between medical features and learn better patient health representations. Existing methods try to estima…
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Liantao Ma, Xinyu Ma, Junyi Gao +7
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from systemic life-threatening problems…
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Liantao Ma, Chaohe Zhang, Yasha Wang +6
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based so…
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
Liantao Ma, Junyi Gao, Yasha Wang +6
Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performanc…