5 papers
Optimizing Clinical Trial Protocols Using EHR-Derived Heterogeneous Treatment Effects
Xiaodi Li, Munhuwan Lee, Pengyang Li +5
Traditional randomized trials often obscure clinically meaningful heterogeneity in treatment response by focusing on average effects. Leveraging real-world data to emulate clinical…
Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching
Xiaodi Li, Yang Xiao, Munhwan Lee +7
Patient-trial matching requires reasoning over long, heterogeneous electronic health records (EHRs) and complex eligibility criteria, posing significant challenges for scalability,…
Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models
MunHwan Lee, Shaika Chowdhury, Xiaodi Li +7
Accurate prediction of treatment outcomes in lung cancer remains challenging due to the sparsity, heterogeneity, and contextual overload of real-world electronic health data. Tradi…
LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation
Xiaodi Li, Shaika Chowdhury, Chung Il Wi +8
Patient matching is the process of linking patients to appropriate clinical trials by accurately identifying and matching their medical records with trial eligibility criteria. We…
Launching Insights: A Pilot Study on Leveraging Real-World Observational Data from the Mayo Clinic Platform to Advance Clinical Research
Yue Yu, Xinyue Hu, Sivaraman Rajaganapathy +15
Backgrounds: Artificial intelligence (AI) is transforming healthcare, yet translating AI models from theoretical frameworks to real-world clinical applications remains challenging.…