24 citations · 55 across the 15 of their papers we have counts for
15 papers
Separating Expert Retention from Autonomous Source Inference in Raw-ECG-Replay-Free Continual ECG Deployment
Yufan Lu, Xinhui Liu, Chenyang Xu +2
In multi-source ECG deployment, new sources may arrive when earlier raw ECGs cannot be retained or replayed. Isolating source-specific classifiers on a frozen backbone prevents par…
LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification
Yi Zhao, Jiajun Gao, Chenyang Xu +2
Deploying deep learning models for automated electrocardiogram classification on resource-constrained wearable devices remains challenging due to high computational costs. To addre…
Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping
Donglin Xie, Qingshuo Zhao, Jingyu Wang +9
Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and poorly suited for multi-night…
Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning
Jiaze Wang, Qinghao Zhao, Zizheng Chen +4
Aortic valve disease (AVD) represents a major public health burden, while its diagnosis relies on echocardiography, which is limited by cost and specialist expertise, restricting s…
Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting
Gangxiong Zhang, Yongchao Long, Yuxi Zhou +2
Accurately predicting mortality risk in intensive care unit (ICU) patients is critical for clinical decision-making. Large language models (LLMs) are increasingly explored for clin…
Artificial Intelligence-Enabled Spirometry for Early Detection of Right Heart Failure
Bin Liu, Qinghao Zhao, Yuxi Zhou +5
Right heart failure (RHF) is a disease characterized by abnormalities in the structure or function of the right ventricle (RV), which is associated with high morbidity and mortalit…
Copy-Paste to Mitigate Large Language Model Hallucinations
Yongchao Long, Xian Wu, Yingying Zhang +3
While Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to generate contextually grounded responses, contextual faithfulness remains challenging as LLMs may…
Artificial Intelligence-derived Cardiotocography Age as a Digital Biomarker for Predicting Future Adverse Pregnancy Outcomes
Jinshuai Gu, Zenghui Lin, Jingying Ma +9
Cardiotocography (CTG) is a low-cost, non-invasive fetal health assessment technique used globally, especially in underdeveloped countries. However, it is currently mainly used to…
Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images
Shanwei Zhang, Deyun Zhang, Yirao Tao +9
Background: Electrocardiograms are indispensable for diagnosing cardiovascular diseases, yet in many settings they exist only as paper printouts stored in multiple recording layout…
SpiroLLM: Finetuning Pretrained LLMs to Understand Spirogram Time Series with Clinical Validation in COPD Reporting
Shuhao Mei, Yongchao Long, Xiaoyu Xiao +6
Chronic Obstructive Pulmonary Disease (COPD), a major chronic respiratory disease with persistent airflow limitation, is a leading global cause of disability and mortality. Respira…
KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease
Yongchao Long, Chao Yang, Gongzheng Tang +5
Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clinical reasoning capabilities. Curr…
Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series
Shuhao Mei, Xin Li, Yuxi Zhou +9
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent fe…
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
Jun Lei, Yuxi Zhou, Xue Tian +5
Atrial Fibrillation (AF) is a common cardiac arrhythmia. Many AF patients experience complications such as stroke and other cardiovascular issues. Early detection of AF is crucial.…
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
Shenda Hong, Yuxi Zhou, Junyuan Shang +2
Background:The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predic…
K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection
Yuxi Zhou, Shenda Hong, Junyuan Shang +4
Atrial Fibrillation (AF) is an abnormal heart rhythm which can trigger cardiac arrest and sudden death. Nevertheless, its interpretation is mostly done by medical experts due to hi…