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医院营养不良管理中的人工智能:系统性审查
Stefano Mancin1, Gaetano Ferrara2, Diego Lopane3
1IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy.
Clinical nutrition ESPEN
|November 2, 2025
概括
人工智能 (AI) 显著提高了医院营养不良的检测比传统方法. 人工智能提高了诊断准确度,减少了延迟,并优化了资源的使用,改善了患者的治疗结果和医院的效率.
科学领域:
- 医疗信息学 医疗信息学
- 临床营养学 临床营养学
- 医疗保健中的人工智能
背景情况:
- 医院营养不良影响30-50%的患者,增加住院时间,并发症和费用.
- 传统的营养不良查方法往往是无效的.
- 人工智能 (AI) 提供了早期检测和改进管理的潜力.
研究的目的:
- 系统地审查人工智能干预措施的有效性,以早期识别和管理医院营养不良.
主要方法:
- 在PubMed,Cochrane图书馆,CINAHL和EMBASE数据库中进行系统搜索.
- 包括评估AI在住院成年人中发现营养不良的研究.
- 使用乔安娜·布里格斯研究所 (JBI) 工具评估研究质量和偏见风险.
主要成果:
- 十二项研究使用了人工智能算法 (例如,随机森林,XGBoost,LightGBM).
- 与传统方法相比,人工智能模型显示出更高的准确性 (AUC>90%).
- 人工智能改善了营养不良的诊断率,减少了延迟,提高了成本效益.
结论:
- 人工智能显示出对准确高效的营养不良检测和管理的巨大潜力.
- 整合人工智能可以改善患者的治疗结果,并节省成本.
- 解决数据质量,员工培训和道德问题对于AI实施至关重要.
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