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败血症管理中的人工智能:临床医生的概述
Elena Giovanna Bignami1, Michele Berdini1, Matteo Panizzi1
1Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Journal of clinical medicine
|January 11, 2025
概括
人工智能 (AI) 和机器学习 (ML) 显示出早期败血症检测和个性化治疗的前景. 然而,假阳性和验证等挑战需要解决,以改善临床结果.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的人工智能
背景情况:
- 败血症是医院死亡的主要原因,需要早期诊断以获得更好的结果.
- 人工智能 (AI) 和机器学习 (ML) 越来越多地被用于败血症预测,诊断和治疗个性化.
- ML模型利用临床数据来预测早期的败血症风险,在症状出现前几个小时.
研究的目的:
- 审查人工智能在败血症管理中的应用.
- 提供关于人工智能治疗败血症的研究概述,评估有效性,局限性和未来潜力.
- 引导临床医生和医疗保健专业人员了解AI在败血症中的作用.
主要方法:
- 按照SPIDER框架进行系统审查.
- 三名审稿人进行了批判性概述,选择了原始和全面的数据.
- 根据重点对选定的文章进行分析:早期预测,诊断,死亡率或治疗改进.
主要成果:
- 确定了194篇文章,其中28篇被选中进行审查.
- 在败血症中AI/ML的应用显示出不同的结果:一些研究报告说死亡率和管理得到改善.
- 挑战包括高假阳性率和现有研究的外部验证不足.
结论:
- 人工智能和ML为增强败血症护理提供了潜力,但进一步验证至关重要.
- 解决虚假阳性等局限性是实现AI在败血症管理中的全部潜力的关键.
- 需要继续进行研究和开发,以优化AI工具用于临床败血症护理.
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