Prediction method for weaning outcomes based on machine learning and electrical impedance tomography
10.19745/j.1003-8868.2023197
- VernacularTitle:基于机器学习和电阻抗断层成像的撤机结局预测方法研究
- Author:
Pu WANG
1
;
Zhan-Qi ZHAO
;
Meng DAI
;
Yi-Fan LIU
;
Jian-An YE
;
Xiang TIAN
;
Ti-Xin HAN
;
Feng FU
Author Information
1. 空军军医大学军事生物医学工程学系,陕西省生物电磁检测与智能感知重点实验室,西安 710032
- Keywords:
electrical impedance tomography;
weaning outcome;
machine learning;
mechanical ventilation
- From:
Chinese Medical Equipment Journal
2023;44(10):1-6
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a method for predicting weaning outcomes based on machine learning and electrical impedance tomography(EIT).Methods Firstly,EIT image features were extracted from a total of 84 samples from 30 patients,and the important features screened with the extreme gradient boosting(XGBoost)algorithm were used as inputs to the model.Secondly,the prediction model was built with six machine learning methods,namely random forest(RF),support vector machines(SVM),XGBoost,gradient boosting decision tree(GBDT),logistic regression(LR)and decision tree(tree).Then the prediction model had its prediction performance evaluated by AUC,accuracy,sensitivity and specificity under imbalanced dataset,over-sampling balanced dataset and random under-sampling balanced dataset.Results In terms of AUC,accuracy and specificity,the model under the over-sampling balanced dataset and the random under-sampling balanced dataset behaved better than that under the imbalanced dataset(P＜0.05);in terms of sensitivity,the difference in model performance between the over-sampling balanced dataset and the imbalanced dataset was not statistically significant(P＞0.05),and the model performance under the random under-sampling balanced dataset decreased when compared with that under the imbalanced dataset(P＜0.05).There were no significant differences between the model performance under the over-sampling balanced dataset and that under the random under-sampling balanced dataset(P＞0.05).The model based on XGBoost behaved the best under the over-sampling balanced dataset,with an AUC of 0.769,an accuracy of 0.808,a sensitivity of 0.938 and a specificity of 0.600.Conclusion The method based on machine learning and EIT predicts weaning outcomes of patients with prolonged mechanical ventilation,and thus can be used for auxiliary decision support for clinicians to determine the appropriate timing of weaning.[Chinese Medical Equipment Journal,2023,44(10):1-6]
