2 citations · 6 across the 11 of their papers we have counts for
12 papers
KMGen: A Skill-based Approach for Synthetic Individual Patient Data Generation
Jalen Jiang, Chufan Gao, Ethan Rasmussen +2
Individual patient data (IPD) from clinical trials is the substrate for survival modeling, meta-analysis, and safety research, yet IPD is rarely released. Prior work has addressed…
Accelerating Reproducible Research in Synthetic EHR Generation
Jalen Jiang, Chufan Gao, Ethan Rasmussen +2
The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy. However, head-to-head comparis…
MedGemma 1.5 Technical Report
Andrew Sellergren, Chufan Gao, Fereshteh Mahvar +39
We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities: high-dimensional medical imagi…
Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding
Chufan Gao, Jintai Chen, Jimeng Sun
Automated tabular understanding and reasoning are essential tasks for data scientists. Recently, Large language models (LLMs) have become increasingly prevalent in tabular reasonin…
Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise
Hanyin Wang, Chufan Gao, Qiping Xu +9
Process-supervised reward models (PRMs) excel at providing step-by-step verification for large language model (LLM) outputs in domains like mathematics and coding. However, their a…
TrialSynth: Generation of Synthetic Sequential Clinical Trial Data
Chufan Gao, Mandis Beigi, Afrah Shafquat +2
Analyzing data from past clinical trials is part of the ongoing effort to optimize the design, implementation, and execution of new clinical trials and more efficiently bring life-…
TTM-RE: Memory-Augmented Document-Level Relation Extraction
Chufan Gao, Xuan Wang, Jimeng Sun
Document-level relation extraction aims to categorize the association between any two entities within a document. We find that previous methods for document-level relation extracti…
Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development
Chufan Gao, Jathurshan Pradeepkumar, Trisha Das +2
Background The cost of drug discovery and development is substantial, with clinical trial outcomes playing a critical role in regulatory approval and patient care. However, access…
Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation
Hanyin Wang, Chufan Gao, Bolun Liu +7
Proprietary Large Language Models (LLMs) such as GPT-4 and Gemini have demonstrated promising capabilities in clinical text summarization tasks. However, due to patient data privac…
Language Interaction Network for Clinical Trial Approval Estimation
Chufan Gao, Tianfan Fu, Jimeng Sun
Clinical trial outcome prediction seeks to estimate the likelihood that a clinical trial will successfully reach its intended endpoint. This process predominantly involves the deve…
PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Chufan Gao, Xulin Fan, Jimeng Sun +1
Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extra…
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
Hanyin Wang, Chufan Gao, Christopher Dantona +2
In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces DRG-LLaMA, an advanced large lan…