机器学习和新兴健康技术在艾滋病毒检测中应用:对2000年至2024年出版的研究进行了图书统计分析
Musa Jaiteh1, Edith Phalane1, Yegnanew A Shiferaw2
1South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Auckland Park Bunting Road Campus, PO Box 524, Auckland Park, Johannesburg, 2006, South Africa, 27 632376425, 27 115591496.
Interactive journal of medical research
|May 22, 2025
概括
机器学习和医疗技术正在推进艾滋病毒检测,但全球一体化落后. 研究显示,高收入国家日益增长的趋势,突出了在艾滋病毒自我检测策略中的更广泛合作和应用的机会.
科学领域:
- 公共卫生 公共卫生
- 生物医学信息学 生物医学信息学
- 卫生技术评估 卫生技术评估
背景情况:
- 全球艾滋病毒检测工作未能达到联合国艾滋病毒/艾滋病联合计划署 (UNAIDS) 的95-95-95目标.
- 机器学习 (ML) 和健康技术为准确识别高风险个体和提高艾滋病毒检测效率提供了潜力.
- 关于将这些技术纳入艾滋病毒检测策略的全球整合,存在重大研究缺口.
研究的目的:
- 对在艾滋病毒检测中应用ML和新兴健康技术的研究进行文献计量分析.
- 从2000年到2024年,检查这些出版物的特点,引用模式和内容.
主要方法:
- 从科学网数据库中对266篇文章进行了图书统计分析.
- 使用了Bibliometrix R包进行数据分析和VOSviewer用于网络可视化.
- 专注于年度增长,引用,关键词,机构,国家,作者和合作模式.
主要成果:
- 年度科学增长率为15.68%,国际共同作者为8.22%.
- 来自高影响力期刊和包括美国,中国,南非,英国和澳大利亚在内的国家的关键贡献.
- 确定了高收入国家机构之间的强有力的合作网络,表明了与低收入国家错过的合作伙伴关系的潜力.
结论:
- ML和健康技术显著提高了艾滋病毒检测的实施,特别是在优先人群中进行艾滋病毒自我检测.
- 研究趋势集中在高收入国家,重点是针对年轻人和关键人群的技术.
- 结果为研究人员提供了洞察力,帮助他们解决研究缺口,并为艾滋病毒检测技术的学术决策提供信息.
关键词:
艾滋病 艾滋病 艾滋病是什么?艾滋病病毒 艾滋病病毒 艾滋病病毒预防艾滋病毒 预防艾滋病毒艾滋病毒检测 艾滋病毒检测申请申请表 申请表 申请表文献表 文献表 文献表圣经计量资料 (Bibliometric bibliometric bibliometric) 是指参考书目中的资料.文献计量分析的分析内容分析 内容分析流行病学流行病学移动健康 移动健康 移动健康 移动健康机器学习是机器学习.移动健康的移动健康这是一个定量性的定量.自己测试的自我测试.有性传播疾病的性病.技术技术的技术技术的技术.更多相关视频
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