146 citations · 258 across the 14 of their papers we have counts for
14 papers
VeriSim: A Configurable Framework for Stress-Testing Medical AI Under Patient Communication Noise
Sina Mansouri, Mohit Marvania, Vibhavari Ashok Shihorkar +5
Medical large language models are typically evaluated on idealized patient cases that do not reflect how real patients communicate. We introduce VeriSim, a patient simulation frame…
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
Yikuan Li, Hanyin Wang, Halid Yerebakan +2
In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their…
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Yikuan Li, Chengsheng Mao, Kaixuan Huang +4
Scarcity of health care resources could result in the unavoidable consequence of rationing. For example, ventilators are often limited in supply, especially during public health em…
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
Zheng Yu, Yikuan Li, Joseph Kim +3
Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test pane…
A Comparative Study of Pretrained Language Models for Long Clinical Text
Yikuan Li, Ramsey M. Wehbe, Faraz S. Ahmad +2
Objective: Clinical knowledge enriched transformer models (e.g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks. One of the core li…
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Chengsheng Mao, Jie Xu, Luke Rasmussen +9
Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from…
Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets
Hanyin Wang, Meghan R. Hutch, Yikuan Li +6
Over 12 billion doses of COVID-19 vaccines have been administered at the time of writing. However, public perceptions of vaccines have been complex. We analyzed COVID-19 vaccine-re…
Multimodal Machine Learning in Precision Health
Adrienne Kline, Hanyin Wang, Yikuan Li +6
As machine learning and artificial intelligence are more frequently being leveraged to tackle problems in the health sector, there has been increased interest in utilizing them in…
Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records
Shishir Rao, Mohammad Mamouei, Gholamreza Salimi-Khorshidi +5
Observational causal inference is useful for decision making in medicine when randomized clinical trials (RCT) are infeasible or non generalizable. However, traditional approaches…
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
Yikuan Li, Ramsey M. Wehbe, Faraz S. Ahmad +2
Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely Cli…
Disparities in Social Determinants among Performances of Mortality Prediction with Machine Learning for Sepsis Patients
Hanyin Wang, Yikuan Li, Andrew Naidech +1
Background Sepsis is one of the most life-threatening circumstances for critically ill patients in the US, while a standardized criteria for sepsis identification is still under de…
Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
Jiyoung Shin, Yikuan Li, Yuan Luo
Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival…
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Yikuan Li, Hanyin Wang, Yuan Luo
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical vis…
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
Yikuan Li, Liang Yao, Chengsheng Mao +3
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will…