推进ICU患者护理,使用机械实时预测模型 减轻VILI的动力
M Ruiz-Botella1, S Manrique2, J Gomez2
1Departament of Chemical Engineering, Universitat Rovira I Virgili, Tarragona, Spain; Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain.
International journal of medical informatics
|June 8, 2024
概括
这项研究开发了一种人工智能模型,用于预测机械通风期间的机械功率 (MP),降低肺损伤的风险. 该模型准确预测MP,使得及时进行调整以防止呼吸机诱导的肺损伤 (VILI).
科学领域:
- 关键护理医学 关键护理医学
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 在ICU中的侵入性机械通风 (IMV) 增加了通风器诱导的肺损伤 (VILI) 的风险.
- 机械功率 (MP) 管理对于减轻VILI至关重要.
- 需要MP的实时预测模型.
研究的目的:
- 开发一个人工智能驱动的机械动力 (MP) 实时预测模型.
- 通过积极的通风管理,减轻呼吸机引起的肺损伤 (VILI).
- 增强机械通风设置的临床决策.
主要方法:
- 追溯观察性研究 (2018-2022年) 与1967名IMV成年患者进行.
- 提取了广泛的患者数据,包括IMV,实验室和人口学变量.
- 开发了一种混合神经网络模型,以提前15分钟预测MP.
主要成果:
- 人工智能模型比基线提高了20%,平均平方误差为2.79.
- 在预测MP超过关键值 (18J/分钟) 时达到94%的准确性.
- 集成模型为临床医生提供实时MP预测.
结论:
- 成功开发并临床整合了MP的AI预测模型.
- 该模型帮助临床医生主动调整通风参数.
- 促进及时干预,以防止机械通风期间的肺损伤.
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