Predictive model for intra-abdominal pressure in critically ill patients based on multiple regression and variational auto-encoders
10.19745/j.1003-8868.2025194
- VernacularTitle:基于多重回归与变分自动编码器的重症患者腹腔内压预测模型研究
- Author:
Yi ZHANG
1
;
Zhi-qin ZHU
;
Wen-lin LI
;
Dong-chu ZHAO
;
Chang LIU
;
Zhi-wei FAN
;
Zhen WANG
;
Lian-yang ZHANG
;
Hao TANG
Author Information
1. 陆军军医大学大坪医院重症医学科,重庆 400042
- Publication Type:Journal Article
- Keywords:
multi regression;
variational auto-encoders;
intra-abdominal pressure;
intra-abdominal hypertension;
deep learning;
critically ill patient
- From:
Chinese Medical Equipment Journal
2025;46(11):10-17
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.
