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在慢性疾病管理中绘制机器学习的景观:一项全面的文献计量研究

Shiying Shen¹, Wenhao Qi¹, Sixie Li¹

¹School of Nursing, Hangzhou Normal University, Hangzhou, China.

|July 29, 2025

概括

机器学习 (ML) 越来越多地用于慢性疾病管理,美国和中国是研究的领导者. 虽然逻辑回归是常见的,但神经网络显示了个性化治疗和风险预测的前景.

科学领域:

图书计量和健康信息学
人工智能在医学中的应用
慢性疾病研究慢性疾病研究

背景情况:

机器学习 (ML) 在慢性疾病管理中的应用正在迅速扩大.
在这个跨学科领域的全球研究成果显示出明显的上升趋势.
了解当前的趋势和贡献对于未来的研究方向至关重要.

研究的目的:

进行全面的图书统计分析,对全球机器学习在慢性疾病管理方面的进展进行综合分析.
确定关键的研究机构,国家和新兴趋势.
确定未来在这个领域进行探索的研究优先事项.

主要方法:

从科学网络数据库中系统地检索了1,242份相关文件.
使用CiteSpace,VOSviewer和RStudio进行文献计量分析,以可视化出版趋势,机构和国家贡献.
专家小组审查分析算法使用情况,疾病重点和应用趋势.

主要成果:

美国和中国是对用于慢性疾病管理的ML研究的主要贡献者.
逻辑回归是最常用的算法,神经网络显示出显著的潜力.
关键的研究领域包括风险预测,诊断和糖尿病和心血管疾病的个性化治疗.

结论:

机器学习是个性化医疗,实时监控和慢性疾病护理中的多式联络数据融合的组成部分.
持续存在的挑战包括有限的协作,模型概括问题和数据隐私问题.
未来的研究应该专注于优化算法和整合多源数据,以增强临床应用.

关键词:

机器学习是机器学习.人工智能的人工智能是人工智能.文献数量计学文献数量计学文献量计学慢性疾病是一种慢性疾病.数据可视化数据可视化疾病管理疾病管理

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