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全球趋势,协作网络和知识绘制 人工智能在败血症诊断和管理中的演变:一个图书识别分析
Zhiyong Lin1,2, Jingyi Deng1,2, Shiming Chen1,2
1Guangdong Medical University, Zhanjiang, Guangdong, China.
Digital health
|November 17, 2025
概括
败血症研究中的人工智能 (AI) 正在快速增长,专注于临床应用和精准医学. 未来的方向强调可解释的AI和多中心验证,以改善患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 败血症是一个关键的全球健康问题,死亡率高,需要改进诊断和治疗策略.
- 传统的败血症诊断和治疗因敏感性问题和患者变异性而面临限制.
- 人工智能 (AI) 为败血症诊断和管理提供了先进的潜力,但对最近趋势的系统分析是有限的.
研究的目的:
- 进行人工智能应用在败血症诊断和管理中的文献分析.
- 确定从2005年到2025年的研究发展模式和前沿动态.
- 为研究人员,临床医生和政策制定者在这个不断变化的领域提供战略指导.
主要方法:
- 从科学网络 (2005-2025) 出版物的图书统计分析.
- 使用VOSviewer,CiteSpace和Bibliometrix进行多维分析.
- 识别出版趋势,领先的国家,研究集群和新兴关键词.
主要成果:
- 自2016年以来,人工智能和败血症研究的指数增长,在2024年达到顶峰.
- 美国在出版物和引用方面处于领先地位,其次是中国.
- 关键的研究领域包括智能预测,分子机制,深度学习早期预警系统,ARDS,MIMIC-IV和免疫透,这表明向多奥米克和精准医学转变.
结论:
- 败血症研究中的AI正在从算法验证转向临床应用,强调可解释的AI和精确的败血症护理.
- 研究轨迹正在从"概念验证"转向证明临床实用性.
- 这些发现强调了需要可解释的模型,多中心验证和基础设施来进行先进的精确性败血症管理.
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