Výsledky vyhledávání - "Abdullah, Hairil"

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Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report

Autor: Ke, YuHe, Jin, Liyuan, Elangovan, Kabilan, Abdullah, Hairil Rizal, Liu, Nan, Sia, Alex Tiong Heng, Soh, Chai Rick, Tung, Joshua Yi Min, Ong, Jasmine Chiat Ling, Ting, Daniel Shu Wei

Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain knowledge in LLMs. This case study presents the development and evalu

Externí odkaz: http://arxiv.org/abs/2402.01733

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Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias

Autor: Ke, Yu He, Yang, Rui, Lie, Sui An, Lim, Taylor Xin Yi, Abdullah, Hairil Rizal, Ting, Daniel Shu Wei, Liu, Nan

Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. Objective: This study explores the

Externí odkaz: http://arxiv.org/abs/2401.14589

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P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data

Autor: Ruan, Yucheng, Lan, Xiang, Tan, Daniel J., Abdullah, Hairil Rizal, Feng, Mengling

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, particularly transformer-based models, have shown remarkable performance in

Externí odkaz: http://arxiv.org/abs/2303.17408

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AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

Autor: Yuan, Han, Xie, Feng, Ong, Marcus Eng Hock, Ning, Yilin, Chee, Marcel Lucas, Saffari, Seyed Ehsan, Abdullah, Hairil Rizal, Goldstein, Benjamin Alan, Chakraborty, Bibhas, Liu, Nan

Background: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among a wide variety of decision-making models for determining the degree of disease deterioration at the bedside. AutoScore was proposed

Externí odkaz: http://arxiv.org/abs/2107.06039

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