1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning
Trisha Das, Zifeng Wang, Afrah Shafquat +4
Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging…
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-…
Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM
Trisha Das, Dina Albassam, Jimeng Sun
Medical dialogue systems (MDS) enhance patient-physician communication, improve healthcare accessibility, and reduce costs. However, acquiring suitable data to train these systems…
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…