21 citations · 48 across the 9 of their papers we have counts for
5 papers · 1 filter
Prediction of mortality and resource utilization in critical care: a deep learning approach using multimodal electronic health records with natural language processing techniques
Yucheng Ruan, Xiang Lan, Daniel J. Tan +2
Background Predicting mortality and resource utilization from electronic health records (EHRs) is challenging yet crucial for optimizing patient outcomes and managing costs in inte…
oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness
Yu He Ke, Liyuan Jin, Kabilan Elangovan +10
Large Language Models (LLMs) show potential for medical applications but often lack specialized clinical knowledge. Retrieval Augmented Generation (RAG) allows customization with d…
Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
YuHe Ke, Liyuan Jin, Kabilan Elangovan +7
Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain…
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
Yu He Ke, Rui Yang, Sui An Lie +4
Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formida…
P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data
Yucheng Ruan, Xiang Lan, Daniel J. Tan +2
Medical tabular data, abundant in Electronic Health Records (EHRs), is a valuable resource for diverse medical tasks such as risk prediction. While deep learning approaches, partic…