VaxBot-HPV:一个基于gpt的聊天机器人,用于回答HPV疫苗相关问题。

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
JAMIA Open Pub Date : 2025-02-19 eCollection Date: 2025-02-01 DOI:10.1093/jamiaopen/ooaf005
Yiming Li, Jianfu Li, Manqi Li, Evan Yu, Danniel Rhee, Muhammad Amith, Lu Tang, Lara S Savas, Licong Cui, Cui Tao
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引用次数: 0

摘要

目的:人乳头瘤病毒(HPV)疫苗是预防和控制HPV引起的疾病的有效措施。然而,广泛存在的错误信息和对疫苗的犹豫态度仍然是影响疫苗使用的重大障碍。本研究的重点是VaxBot-HPV的开发,这是一个聊天机器人,旨在通过提供有关HPV疫苗的信息和回答问题来提高健康素养和促进疫苗接种。方法:构建VaxBot-HPV知识库(KB),该知识库由451篇来自生物医学文献和网络的HPV疫苗相关文献组成。我们从知识库中提取了202个问题对,从GPT-4中提取了39个问题,用于培训和测试。为了全面了解基于gpt的聊天机器人的能力和潜力,本研究涉及3个模型:GPT-3.5, VaxBot-HPV和GPT-4。评价标准包括答案的相关性和可信度。结果:与基线相比,VaxBot-HPV在答案相关性和可靠性方面表现出优越的性能。对于知识库中的测试问题,它的答案相关性得分为0.85,忠实度得分为0.97。同样,在gpt生成的问题上,它的答案相关性得分为0.85,忠实度得分为0.96。讨论:VaxBot-HPV展示了在医疗保健中微调大型语言模型的有效性,在准确性和相关性方面优于通用GPT模型。微调减轻了幻觉和错误信息,确保了HPV疫苗接种的可靠信息,同时允许动态和量身定制的反应。具体的微调,除了问答对,还包括上下文,使VaxBot-HPV能够提供答案背后的解释和推理,提高透明度和用户信任。结论:本研究强调了利用大型语言模型和微调技术开发医疗保健应用聊天机器人的重要性,这对改善医学教育和公共卫生交流具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VaxBot-HPV: a GPT-based chatbot for answering HPV vaccine-related questions.

Objective: Human Papillomavirus (HPV) vaccine is an effective measure to prevent and control the diseases caused by HPV. However, widespread misinformation and vaccine hesitancy remain significant barriers to its uptake. This study focuses on the development of VaxBot-HPV, a chatbot aimed at improving health literacy and promoting vaccination uptake by providing information and answering questions about the HPV vaccine.

Methods: We constructed the knowledge base (KB) for VaxBot-HPV, which consists of 451 documents from biomedical literature and web sources on the HPV vaccine. We extracted 202 question-answer pairs from the KB and 39 questions generated by GPT-4 for training and testing purposes. To comprehensively understand the capabilities and potential of GPT-based chatbots, 3 models were involved in this study: GPT-3.5, VaxBot-HPV, and GPT-4. The evaluation criteria included answer relevancy and faithfulness.

Results: VaxBot-HPV demonstrated superior performance in answer relevancy and faithfulness compared to baselines. For test questions in KB, it achieved an answer relevancy score of 0.85 and a faithfulness score of 0.97. Similarly, it attained scores of 0.85 for answer relevancy and 0.96 for faithfulness on GPT-generated questions.

Discussion: VaxBot-HPV demonstrates the effectiveness of fine-tuned large language models in healthcare, outperforming generic GPT models in accuracy and relevance. Fine-tuning mitigates hallucinations and misinformation, ensuring reliable information on HPV vaccination while allowing dynamic and tailored responses. The specific fine-tuning, which includes context in addition to question-answer pairs, enables VaxBot-HPV to provide explanations and reasoning behind its answers, enhancing transparency and user trust.

Conclusions: This study underscores the importance of leveraging large language models and fine-tuning techniques in the development of chatbots for healthcare applications, with implications for improving medical education and public health communication.

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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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