自然语言处理和健康社会决定因素的大型语言模型的应用:系统审查议定书
Swati Rajwal1, Ziyuan Zhang2, Yankai Chen3
1Department of Computer Science, Emory University, Atlanta, GA, United States.
JMIR research protocols
|January 21, 2025
概括
本系统性审查综合了自然语言处理 (NLP) 对于健康社会决定因素 (SDOH) 的应用. 它确定了知识差距和未来的研究方向,以改善公共卫生中的NLP模型.
科学领域:
- 公共卫生 公共卫生
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 自然语言处理 (NLP) 为在文本数据中分析健康的社会决定因素 (SDOH) 提供了创新的方法.
- 对于SDOH的NLP现有文献是跨学科的碎片化,需要一个整合的概述.
- 识别知识缺口和未来的研究方向对于推动这一领域的发展至关重要.
研究的目的:
- 系统地审查和突出NLP技术,包括大型语言模型,在SDOH研究中应用.
- 巩固现有关于NLP应用的知识,用于SDOH.
- 通过识别差距和提出研究问题,为未来的研究提供信息.
主要方法:
- 在七个主要数据库 (PubMed, Web of Science, IEEE Xplore, Scopus, PsycINFO, HealthSource: 学术护理, ACL Anthology) 执行了一项全面的搜索策略.
- 包括在2014年至2024年期间以英语出版的研究.
- 采用了三名审稿人的独立选,另外两名审稿人的冲突解决,以及引用的研究的前搜索.
主要成果:
- 搜索策略于2024年8月在指定数据库中执行.
- 截至2024年12月,标题和摘要选正在进行中.
- 预计完整的结果将在2025年初进行同行评审出版.
结论:
- 这一系统性审查将在各种文本数据集中对SDOH任务的NLP应用进行全面分析.
- 对方法,工具和结果的评估将确定当前的知识差距.
- 结果将指导未来的研究,旨在增强SDOH的NLP模型并改善公共卫生结果.
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