重量确定关键词网络技术:用于系统审查的关键词识别的结构化方法
Sasidharan Sivakumar1, Gowardhan Sivakumar2
1Indian Council of Medical Research, New Delhi, India.
Healthcare informatics research
|February 20, 2025
概括
重量识别的关键词网络 (WINK) 技术通过改进关键词选择来增强系统性审查. 这种方法显著增加了相关文章的数量,确保了更全面的证据综合.
科学领域:
- 图书统计学 图书统计学
- 信息科学 信息科学 信息科学
- 系统审查方法论 系统审查方法论
背景情况:
- 系统审查需要有效和精确的关键词选择,以进行彻底的证据综合.
- 当前的关键词选择方法可能缺乏严格性,可能导致不完整或不准确的发现.
研究的目的:
- 开发权重识别的关键词网络 (WINK) 技术,以进行高效的系统审查.
- 通过改进关键字选择,提高证据综合的彻底性和精度.
主要方法:
- 开发了WINK方法,使用网络可视化图表来分析关键字互连.
- 综合计算分析与主题专家洞察力关键字相关性.
- 排除了具有有限网络力量的关键词,专注于高权重术语.
- 使用通过WINK. identified识别的医学主题标题 (MeSH) 构建的搜索字符串.
主要成果:
- 基于WINK技术的搜索字符串发现环境污染物/内分泌功能 (Q1) 的文章比传统方法多了69.81%.
- 与传统方法相比,WINK技术发现了系统健康/口腔健康 (Q2) 的文章数量增加了26.23%.
- 在识别相关研究以综合综合证据方面表现出更高的有效性.
结论:
- WINK技术确保了系统审查中的全面证据综合和准确性.
- 优先考虑重量较高的关键词和网络可视化可以提高系统审查结果.
- 代表了系统性审查方法论的重大进步,用于强大和高效的关键词选择.
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