1 citations · 1 across the 7 of their papers we have counts for
7 papers
POET: Protocol Optimization via Eligibility Tuning
Trisha Das, Katherine Kero, Dorinda Schumann +4
Eligibility criteria (EC) are essential for clinical trial design, yet drafting them remains a time-intensive and cognitively demanding task for clinicians. Existing automated appr…
: Benchmarking Eligibility Criteria Amendments in Clinical Trials
Trisha Das, Mandis Beigi, Jacob Aptekar +1
Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component. We introduce…
SECRET: Semi-supervised Clinical Trial Document Similarity Search
Trisha Das, Afrah Shafquat, Beigi Mandis +2
Clinical trials are vital for evaluation of safety and efficacy of new treatments. However, clinical trials are resource-intensive, time-consuming and expensive to conduct, where e…
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…
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…
TopicAdapt- An Inter-Corpora Topics Adaptation Approach
Pritom Saha Akash, Trisha Das, Kevin Chen-Chuan Chang
Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However,…