29 citations · 92 across the 18 of their papers we have counts for
18 papers
Unveiling and Mitigating Bias in Mental Health Analysis with Large Language Models
Yuqing Wang, Yun Zhao, Sara Alessandra Keller +4
The advancement of large language models (LLMs) has demonstrated strong capabilities across various applications, including mental health analysis. However, existing studies have f…
Language and Multimodal Models in Sports: A Survey of Datasets and Applications
Haotian Xia, Zhengbang Yang, Yun Zhao +7
Recent integration of Natural Language Processing (NLP) and multimodal models has advanced the field of sports analytics. This survey presents a comprehensive review of the dataset…
RUPBench: Benchmarking Reasoning Under Perturbations for Robustness Evaluation in Large Language Models
Yuqing Wang, Yun Zhao
With the increasing use of large language models (LLMs), ensuring reliable performance in diverse, real-world environments is essential. Despite their remarkable achievements, LLMs…
SportQA: A Benchmark for Sports Understanding in Large Language Models
Haotian Xia, Zhengbang Yang, Yuqing Wang +7
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP). This holds particular significance in the…
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records
Yuqing Wang, Malvika Pillai, Yun Zhao +2
In the high-stakes realm of healthcare, ensuring fairness in predictive models is crucial. Electronic Health Records (EHRs) have become integral to medical decision-making, yet exi…
Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
Yuqing Wang, Yun Zhao
The burgeoning interest in Multimodal Large Language Models (MLLMs), such as OpenAI's GPT-4V(ision), has significantly impacted both academic and industrial realms. These models en…
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction
Yuqing Wang, Prashanth Vijayaraghavan, Ehsan Degan
Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors a…
TRAM: Benchmarking Temporal Reasoning for Large Language Models
Yuqing Wang, Yun Zhao
Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by…
Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera
Haotian Xia, Rhys Tracy, Yun Zhao +3
This paper presents PathFinder and PathFinderPlus, two novel end-to-end computer vision frameworks designed specifically for advanced setting strategy classification in volleyball…
Metacognitive Prompting Improves Understanding in Large Language Models
Yuqing Wang, Yun Zhao
In Large Language Models (LLMs), there have been consistent advancements in task-specific performance, largely influenced by effective prompt design. Recent advancements in prompti…
An Empirical Study on the Robustness of the Segment Anything Model (SAM)
Yuqing Wang, Yun Zhao, Linda Petzold
The Segment Anything Model (SAM) is a foundation model for general image segmentation. Although it exhibits impressive performance predominantly on natural images, understanding it…
Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding
Yuqing Wang, Yun Zhao, Linda Petzold
Large language models (LLMs) have made significant progress in various domains, including healthcare. However, the specialized nature of clinical language understanding tasks prese…
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning
Yuqing Wang, Yun Zhao, Linda Petzold
As critically ill patients frequently develop anemia or coagulopathy, transfusion of blood products is a frequent intervention in the Intensive Care Units (ICU). However, inappropr…
Enhancing Transformer Efficiency for Multivariate Time Series Classification
Yuqing Wang, Yun Zhao, Linda Petzold
Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy. However, for large-scale (either high-dimensional or long-sequenti…
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis
Yuqing Wang, Yun Zhao, Rachael Callcut +1
Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer…
Empirical Quantitative Analysis of COVID-19 Forecasting Models
Yun Zhao, Yuqing Wang, Junfeng Liu +5
COVID-19 has been a public health emergency of international concern since early 2020. Reliable forecasting is critical to diminish the impact of this disease. To date, a large num…
Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients
Yuqing Wang, Yun Zhao, Rachael Callcut +1
Multiple organ failure (MOF) is a life-threatening condition. Due to its urgency and high mortality rate, early detection is critical for clinicians to provide appropriate treatmen…
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients
Yun Zhao, Qinghang Hong, Xinlu Zhang +3
Survival analysis is a technique to predict the times of specific outcomes, and is widely used in predicting the outcomes for intensive care unit (ICU) trauma patients. Recently, d…