Analysis of Ventilator Alarms Based on Machine Learning
Objective To study the ventilation alarms of ventilators in clinical use by applying machine learning methods,obtain the important parameters affecting the alarms and the alarm prediction model,identify invalid alarms and give clinical hints,so that the clinic can respond to the ventilator alarms efficiently to avoid the negative effects of alarm fatigue and other negative impacts.Methods A respiratory data management platform was established that conformed to standard data processes.According to the alarm information of single center ventilator,the characteristic values were analyzed and the important parameters were sorted.Hyperparameter optimization modeling was used to predict the true or false alarm.The confusion matrix and receiver operating characteristic(ROC)were used to validate the machine learning model.Results The test set of 5936 ventilation alarms was evaluated,with 88%invalid alarms rate(recall rate was 0.88).The model accuracy was 0.94,and the precision was 0.78,the area under ROC curve was 0.92.The F1 score was 0.82.Conclusion The use of machine learning facilitates clinical single-center data modeling can timely analyze and obtain the important parameters and alarm predictions of the real alarm of the ventilator,and through the ventilator data management platform,it can effectively prompt the clinical invalid alarms,thus reducing the pressure of the alarms on the healthcare personnel and improving the quality of medical care.
国家科技期刊平台
NSTL
万方数据
