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开发一个混合LSTM与黑猩猩优化算法,用于压力通风器预测

Fatma Refaat Ahmed^1,2, Samira Ahmed Alsenany³, Sally Mohammed Farghaly Abdelaliem⁴

¹Department of Nursing, College of Health Sciences, University of Sharjah, Sharjah, UAE.

Scientific reports

|November 28, 2023

概括

这项研究开发了一个LSTM-ChoA模型,以准确预测机械通风机压力. 该模型显著减少了预测错误,改善了在呼吸辅助期间的患者护理.

科学领域:

生物医学工程生物医学工程
计算医学是一种计算医学.
医疗保健中的人工智能

背景情况:

机械通风对于严重的肺部疾病至关重要,特别是在流行病期间.
呼吸机参数的自主调整对于最佳的患者治疗至关重要.
需要准确预测通风机压力,以确保安全有效的呼吸辅助.

研究的目的:

开发一个机械通风机压力的预测模型.
使用先进的计算技术来提高预测准确度.
通过精确的压力预测来改善患者管理.

主要方法:

使用长短期记忆 (LSTM) 计算模型预测通风机压力.
集成了黑猩猩优化方法 (ChoA) 来优化LSTM的超参数.
开发了LSTM-ChoA模型,以提高预测准确度.

主要成果:

该LSTM-ChoA模型显著超过现有的优化算法 (GWO,WOA,PSO) 和回归模型 (KNN,RF,SVM).
实现了平均平方误差 (MSE) 的大幅降低:~14.8%与GWO相比,~60%与PSO和WOA相比.
差异分析 (ANOVA) 证实了p值为0.000的统计显著性.

结论:

LSTM-ChoA模型提供了一个非常准确和统计学上显著的方法来预测机械通风机压力.
这种预测能力可以改善呼吸机的自主控制,并改善患者的治疗结果.
该研究强调了将LSTM与ChoA结合用于复杂的生理建模和医疗设备优化的潜力.

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