17 citations · 17 across the 5 of their papers we have counts for
5 papers
MoMA: A Mixture-of-Multimodal-Agents Architecture for Enhancing Clinical Prediction Modelling
Jifan Gao, Mahmudur Rahman, John Caskey +8
Multimodal electronic health record (EHR) data provide richer, complementary insights into patient health compared to single-modality data. However, effectively integrating diverse…
Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs
Skatje Myers, Dmitriy Dligach, Timothy A. Miller +6
Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health re…
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Maya Kruse, Majid Afshar, Saksham Khatwani +3
Large language models (LLMs) often behave inconsistently across inputs, indicating uncertainty and motivating the need for its quantification in high-stakes settings. Prior work on…
Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability
Yanjun Gao, Skatje Myers, Shan Chen +7
Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains l…
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies
Skatje Myers, Timothy A. Miller, Yanjun Gao +4
Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation…