2 citations · 6 across the 11 of their papers we have counts for
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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…