实时预测重症监护病人的敏度和治疗需求,使用状态空间建模
Miguel Contreras1,2, Brandon Silva2,3, Benjamin Shickel2,4
1Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Nature communications
|August 8, 2025
概括
一个新的AI模型APRICOT-M可以在4小时内预测重症监护室 (ICU) 患者的不稳定性和治疗需求. 该工具通过准确预测临床恶化,支持早期干预,优于现有方法.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 重症监护室 (ICU) 患者需要及时识别临床恶化,以便及时进行干预.
- 现有的人工智能 (AI) 模型通常使用死亡率作为度的代理,无法直接预测不稳定性或治疗需求.
研究的目的:
- 介绍APRICOT-M,一种新的状态空间模型,旨在实时预测ICU患者的敏度结果,转变以及在四小时窗口内需要维持生命治疗的需求.
- 通过专注于直接预测临床不稳定性和治疗需求,解决当前人工智能模型的局限性.
主要方法:
- 开发了APRICOT-M,一种状态空间模型,集成了各种患者数据,包括生命体征,实验室,药物和评估得分.
- 该模型有效地处理稀疏和不规则的采样数据.
- 在55家医院的14万多名ICU入院患者中接受培训,并通过外部和实时数据集进行验证.
主要成果:
- 与已建立的临床分数相比,子M在预测死亡率和临床不稳定性方面表现优越.
- 该模型准确地预测了在四小时内需要维持生命治疗的需求.
- 医生反表明,该模型的警报在大量情况下是可行的和及时的.
结论:
- APRICOT-M在实时ICU患者监测和敏度评估方面提供了一个有希望的进步.
- 该模型有可能显著提高重症监护机构干预的及时性和信息性.
- 这种人工智能工具可以帮助临床医生为重症患者做出更早,更基于证据的决定.
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