Transforming Healthcare Education: Harnessing Large Language Models for Frontline Health Worker Capacity Building using Retrieval-Augmented Generation

Transforming Healthcare Education: Harnessing Large Language Models for Frontline Health Worker Capacity Building using Retrieval-Augmented Generation
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转变医疗保健教育:利用大型语言模型利用检索增强生成技术进行一线卫生工作者的能力建设

DOI:
10.1101/2023.12.15.23300009
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发表时间:
2023
期刊:
--
影响因子:
--
通讯作者:
Al Ghadban Y
Al Ghadban Y
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--
文献类型:
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作者:
Al Ghadban Y

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近年来,大型语言模型(llm)已成为包括医学教育和医疗保健在内的多个领域的变革力量。本文介绍了使用基于检索增强生成(RAG)的模型在低收入和中等收入国家加强医疗保健教育的实际应用的案例研究。本文所描述的SMARThealthGPT模型源于需要可获取和与当地相关的医疗信息,以帮助社区卫生工作者提供高质量的孕产妇保健。我们描述了完整的RAG管道的开发过程,包括创建印度妊娠相关指南知识库,知识嵌入检索,参数选择和优化以及答案生成。本案例研究强调了法学硕士在建设一线医疗工作者能力和加强基于指南的健康教育方面的潜力;并为资源有限环境下的类似应用提供了见解。它可以作为机器学习科学家、教育工作者、医疗保健专业人员和决策者的参考,旨在利用法学硕士的力量进行实质性的教育改进。
In recent years, large language models (LLMs) have emerged as a transformative force in several domains, including medical education and healthcare. This paper presents a case study on the practical application of using retrieval-augmented generation (RAG) based models for enhancing healthcare education in low- and middle-income countries. The model described in this paper, SMARThealthGPT, stems from the necessity for accessible and locally relevant medical information to aid community health workers in delivering high-quality maternal care. We describe the development process of the complete RAG pipeline, including the creation of a knowledge base of Indian pregnancy-related guidelines, knowledge embedding retrieval, parameter selection and optimization, and answer generation. This case study highlights the potential of LLMs in building frontline healthcare worker capacity and enhancing guideline-based health education; and offers insights for similar applications in resource-limited settings. It serves as a reference for machine learning scientists, educators, healthcare professionals, and policymakers aiming to harness the power of LLMs for substantial educational improvement.
DOI: 10.3389/fgwh.2021.620759
发表时间: 2021
期刊: Frontiers in global women's health
影响因子: --
作者:
Nagraj S;Kennedy SH;Jha V;Norton R;Hinton L;Billot L;Rajan E;Arora V;Praveen D;Hirst JE
通讯作者: Hirst JE