为了可靠地预测手术内低血压:基于深度学习和MAP衍生的方法的跨中心评估
Nada Chaari1, Greg Winski2,3, Magnus Hallbäck4
1Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Hälsovägen 11, Huddinge, 141 52, Sweden. nadcha@kth.se.
预测手术内低血压 (IOH) 的人工智能 (AI) 工具往往高估了可靠性. 这项研究引入了一个更现实的AI验证框架,提高了临床适用性.
科学领域:
- 麻醉学 麻醉学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 术内低血压 (IOH) 会增加心脏和脏并发症的风险.
- 目前用于IOH预测的AI工具可能由于数据选择偏差而夸大了现实世界的可靠性.
- 可靠的IOH预测对于手术期间的患者安全至关重要.
研究的目的:
- 引入一个框架,以提高预测手术内低血压的AI模型的可靠性.
- 解决数据集特定偏差,并提高IOH的AI模型的概括性.
- 为了使人工智能工具在临床使用中得到更现实的验证.
主要方法:
- 将边界血压病例 (65-75 mmHg,"灰色区域") 纳入分析.
- 将AI模型的性能与简单的平均动脉压 (MAP) 值进行比较.
- 在多种不同的外科队列,中心和人口统计学中验证模型,标准化MAP差异以减轻选择偏差.
主要成果:
- 人工智能模型在模两可的"灰色区域"案例中表现优于MAP值方法.
- 当低血压和非低血压病例有明显的MAP分离时,表现相似.
- 在边界病例中训练的模型更好地将数据集概括到更清晰的数据集;相反的情况不是真的.
- 标准化MAP差异揭示了一些数据集的膨胀性能是由于选择偏差.
- 在老年患者中训练的模型更好地对年轻人群进行了概括; ASA分类的影响最小.
结论:
- 现实的验证框架对于弥合人工智能研究和临床实践之间的差距至关重要.
- 数据集的特点,特别是边界病例的包含,显著影响AI模型的概括性.
- 年龄影响人工智能模型的概括性,这表明需要在验证时考虑特定年龄的因素.
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