28 citations · 32 across the 9 of their papers we have counts for
10 papers
Robust Multimodal Sentiment Analysis via Double Information Bottleneck
Huiting Huang, Tieliang Gong, Kai He +3
Multimodal sentiment analysis has received significant attention across diverse research domains. Despite advancements in algorithm design, existing approaches suffer from two crit…
Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI
Dilruk Perera, Gousia Habib, Qianyi Xu +4
Reinforcement learning (RL) marks a fundamental shift in how artificial intelligence is applied in healthcare. Instead of merely predicting outcomes, RL actively decides interventi…
Prediction of mortality and resource utilization in critical care: a deep learning approach using multimodal electronic health records with natural language processing techniques
Yucheng Ruan, Xiang Lan, Daniel J. Tan +2
Background Predicting mortality and resource utilization from electronic health records (EHRs) is challenging yet crucial for optimizing patient outcomes and managing costs in inte…
MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations
Deyun Zhang, Xiang Lan, Shijia Geng +4
Electrocardiogram (ECG) plays a foundational role in modern cardiovascular care, enabling non-invasive diagnosis of arrhythmias, myocardial ischemia, and conduction disorders. Whil…
DivScore: Zero-Shot Detection of LLM-Generated Text in Specialized Domains
Zhihui Chen, Kai He, Yucheng Huang +2
Detecting LLM-generated text in specialized and high-stakes domains like medicine and law is crucial for combating misinformation and ensuring authenticity. However, current zero-s…
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
Jiaxing Xu, Kai He, Yue Tang +5
Neurological conditions, such as Alzheimer's Disease, are challenging to diagnose, particularly in the early stages where symptoms closely resemble healthy controls. Existing brain…
GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images
Xiang Lan, Feng Wu, Kai He +3
While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, they still face two key limitations: (1) insufficient multimodal synergy between t…
Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution
Yucheng Ruan, Xiang Lan, Jingying Ma +3
Tabular data, a prevalent data type across various domains, presents unique challenges due to its heterogeneous nature and complex structural relationships. Achieving high predicti…
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
Jihai Zhang, Xiang Lan, Xiaoye Qu +3
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data. However, a major challenge that hinders both unimodal and mu…
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
Kai He, Rui Mao, Qika Lin +4
The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern due to their ability to effectively respond to freetext queries w…