定义成功机械通风断奶的预测因素,使用数据挖掘过程和人工智能
Juliette Menguy1, Kahaia De Longeaux1,2, Laetitia Bodenes1
1Medical Intensive Care Unit, CHRU de la Cavale Blanche, Bvd Tanguy-Prigent, 29609, Brest Cedex, France.
Scientific reports
|November 22, 2023
概括
在重症监护室 (ICU) 预测输出管成功至关重要. 这项研究确定了关键的生理参数,并开发了一个人工智能模型,以准确预测成功的机械通风断奶,减少不良事件.
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
- 数据科学数据科学数据科学
背景情况:
- 机械通风断奶是复杂的,失败的输出导致增加的发病率和死亡率.
- 确定输出管成功的预测因素对于优化重症监护室 (ICU) 患者的治疗结果至关重要.
研究的目的:
- 通过数据挖掘和人工智能,识别出输出成功的预测因素.
- 开发一个动态预测模型,用于预测机械通风的成功断奶.
主要方法:
- 从接受机械通风断奶的成年患者身上收集生理和生物医学信号的前性数据收集.
- 在自发呼吸试验 (SBT) 中分析血液动力学和呼吸系统参数.
- 开发一个预测模型,结合诸如早期预警分数氧气 (EWSO2),平均动脉压,心率变化,体重指数 (BMI),闭塞压力 (P0.1) 和LF/HF比率等参数.
主要成果:
- 早期预警氧气评分 (EWSO2) 有效地区分了可能在72小时和7天 (AUC=0.80) 后成功抽管的患者.
- 确定的主要预测因素包括BMI,P0.1,LF/HF比率 (SBT前) 和SBT期间的心率.
- 开发的AI模型在预测输出管成功方面表现出62%和83%的全球性能.
结论:
- 数据挖掘和人工智能可以识别出输出成功的独立预测因素.
- 使用人工智能的动态预测模型可以帮助临床医生更好地区分患者进行成功的输出管.
- 更好的预测输出成功可以提高临床性能,减少ICU的不良事件.
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