1 citations · 1 across the 14 of their papers we have counts for
14 papers
A Rubric-Guided Large Language Model Solution for Opioid Use Disorder Computable Phenotyping
Mengxian Lyu, Paredes Pardo, Cheng Peng +7
Opioid use disorder (OUD) remains a public health crisis in the United States, yet it is difficult to identify from electronic health records (EHRs) because missing diagnosis codes…
An Agentic Generative Large Language Model for Treatment Planning of Colorectal Cancer
Mengxian Lyu, Cheng Peng, Tim Jang +18
Treatment planning in precision oncology requires synthesizing heterogeneous patient information with rapidly evolving clinical guidelines to ensure guideline-concordant care. Whil…
Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives
Aman Pathak, Cheng Peng, Mengxian Lyu +8
Information extraction from pathology reports is essential for cancer staging, tumor registry population. Yet key data remains embedded in narrative reports, making manual extracti…
Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives
Ziyi Chen, Mengxian Lyu, Cheng Peng +1
Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language…
Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Models
Ziyi Chen, Yasir Khan, Mengyuan Zhang +7
Human immunodeficiency virus (HIV)-related stigma is a critical psychosocial determinant of health for people living with HIV (PLWH), influencing mental health, engagement in care,…
Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models
Mengxian Lyu, Cheng Peng, Ziyi Chen +3
Automatic summarization of radiology reports is an essential application to reduce the burden on physicians. Previous studies have widely used the "pre-training, fine-tuning" strat…
A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP
Cheng Peng, Mengxian Lyu, Ziyi Chen +1
Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple…
Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging
Mengxian Lyu, Cheng Peng, Ziyi Chen +3
Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a signi…
MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine
Kai Zhang, Zhengqing Yuan, Cheng Peng +10
Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are ei…
A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning
Cheng Peng, Xinyu Dong, Mengxian Lyu +3
Natural language processing (NLP) is a key technology to extract important patient information from clinical narratives to support healthcare applications. The rapid development of…
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning
Cheng Peng, Kai Zhang, Mengxian Lyu +3
To advance biomedical vison-language model capabilities through scaling up, fine-tuning, and instruction tuning, develop vision-language models with improved performance in handlin…
Natural Language Generation in Healthcare: A Review of Methods and Applications
Mengxian Lyu, Xiaohan Li, Ziyi Chen +4
Natural language generation (NLG) is the key technology to achieve generative artificial intelligence (AI). With the breakthroughs in large language models (LLMs), NLG has been wid…
UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Models
Mengxian Lyu, Cheng Peng, Daniel Paredes +4
Automatic generation of discharge summaries presents significant challenges due to the length of clinical documentation, the dispersed nature of patient information, and the divers…
Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
Mengxian Lyu, Cheng Peng, Xiaohan Li +3
Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-…