Department of Nursing, College of Health Sciences, University of Sharjah, Sharjah, UAE.
Critical Care and Emergency Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
Sci Rep. 2023 Nov 27;13(1):20927. doi: 10.1038/s41598-023-47837-8.
The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.
在管理患有严重肺部疾病的个体时，机械通气的应用至关重要。在大流行期间，必须制造能够在治疗过程中自动调整参数的呼吸机。为了满足这一要求，进行了一项研究调查，旨在预测呼吸机对患者施加的压力大小。上述预测是通过对包括呼吸机特性和患者医疗状况在内的许多变量进行全面分析得出的。该分析使用了一种称为长短期记忆（LSTM）的复杂计算模型。为了提高 LSTM 模型的预测准确性，研究人员使用了 Chimp Optimization 方法（ChoA）。LSTM 和 ChoA 的集成产生了 LSTM-ChoA 模型，该模型成功解决了 LSTM 模型的超参数选择问题。实验结果表明，与替代优化算法（鲸鱼灰狼优化算法（GWO）、优化算法（WOA）和粒子群优化算法（PSO））相比，LSTM-ChoA 模型表现出更好的性能。此外，LSTM-ChoA 模型在准确预测呼吸机压力方面优于回归模型，包括 K-最近邻（KNN）回归器、随机森林（RF）回归器和支持向量机（SVM）回归器。研究结果表明，所提出的预测模型 LSTM-ChoA 具有较小的均方误差（MSE）值。具体而言，与 GWO 相比，ChoA 的 MSE 降低了约 14.8%。此外，与 PSO 和 WOA 相比，ChoA 的 MSE 降低了约 60%。此外，方差分析（ANOVA）的结果表明，LSTM-ChoA 模型的 p 值为 0.000，小于预定的显著性水平 0.05。这表明 LSTM-ChoA 模型的结果具有统计学意义。
