基于人工智能的风扇相关肺炎预测模型:范围审查
Jinbo Zhang1,2, Pingping Yang1,2, Lu Zeng1,2
1Nursing Department, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
JMIR medical informatics
|May 21, 2024
概括
人工智能 (AI) 在预测呼吸机相关肺炎 (VAP) 方面表现有前途. 目前用于VAP预测的AI模型主要使用机器学习和文本数据,但需要进一步研究才能临床实施.
科学领域:
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
- 医疗保健中的人工智能
背景情况:
- 呼吸机相关肺炎 (VAP) 是机械呼吸的严重并发症,影响患者的治疗结果.
- 人工智能 (AI) 提供先进的数据挖掘功能,用于预测VAP.
- 早期识别高风险患者对于有效的VAP管理至关重要.
研究的目的:
- 审查现有的基于人工智能的模型,用于VAP预测.
- 为未来临床应用提供参考,用于识别高风险VAP群体.
- 综合目前关于人工智能用于VAP预测的研究.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 在多个数据库中进行了搜索,包括PubMed,Embase和Web of Science.
- 数据的提取和合成由两个独立的审稿人进行.
主要成果:
- 包括使用人工智能用于VAP预测的11项研究.
- 所有审查的研究都集中在VAP发生预测上,不包括预后.
- 机器学习,特别是随机森林,是主要的算法;使用文本数据,但不是图像数据. 公共数据库是常见的数据来源,大多数研究的样本大小在1000以下. 没有使用深度学习和大型语言模型. 所有研究都只报告了内部验证,缺乏现实世界的实施策略.
结论:
- 与传统方法相比,人工智能模型显示了VAP的优异预测性能.
- 人工智能将成为VAP风险预测的重要工具.
- 需要进一步的研究来解决AI驱动的VAP预测模型的临床实施和实际指导.
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