Development and comparison of convolutional neural network and logistic regression models for predicting anti-tuberculosis drug-induced liver injury
10.3760/cma.j.cn114015-20230520-00378
- VernacularTitle:预测抗结核药物性肝损伤的卷积神经网络与logistic回归模型的建立与比较
- Author:
Lu XU
1
;
Yuan WEI
;
Fuhui LU
;
Xingbei ZHOU
;
Jing WU
Author Information
1. 镇江市第三人民医院/江苏大学附属镇江三院药剂科，镇江 212021
- Publication Type:Journal Article
- Keywords:
Antitubercular agents;
Chemical and drug induced liver injury;
Artificial neural networks;
Deep learning;
Logistic models;
Risk factors;
Prediction model
- From:
Adverse Drug Reactions Journal
2023;25(12):705-711
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop 2 prediction models for anti-tuberculosis drug-induced liver injury (ATB-DILI) based on convolutional neural network (CNN) and multiple logistic regression, and to evaluate and compare the performance of the 2 models.Methods:The clinical and laboratory test data of inpatients in the Third People′s Hospital of Zhenjiang, Jurong People's Hospital, and the Third People′s Hospital of Danyang from January 1, 2019 to October 31, 2022 were collected. According to whether ATB-DILI occurred, patients were divided into with and without ATB-DILI groups, and the clinical characteristics of the 2 groups were compared. The patients were randomly divided into training set and test set according to a ratio of 7∶3 by random number table method. Based on data in the training set, multiple logistic regression and CNN were used to develop ATB-DILI prediction models; based on data in the training and test sets, the accuracy of the 2 models in predicting ATB-DILI was verified. The receiver operating characteristic (ROC) curve was drawn, and the sensitivity, specificity, Youden index and area under the curve (AUC) of the 2 models were compared.Results:A total of 3 012 patients were included in the study, of which 294 (9.76%) were diagnosed with ATB-DILI; 2 108 patients were in the training set and 904 in the test set. The results of multiple logistic regression analysis showed that age, history of liver diseases, hypoalbuminemia, and no preventive use of liver protection drugs were independent risk factors for the occurrence of ATB-DILI. Based on these risk factors, multiple logistic regression model equations were constructed. The results of deep learning and analyzing the patient data of the training set by CNN showed that the top 5 risk factors that had the greatest impact on the occurrence of ATB-DILI were history of liver disease, age, no preventive use of liver protection drugs, hypoalbuminemia, and alcohol consumption. The CNN model was constructed according to the top 5 risk factors. The total accuracy in predicting the occurrence of ATB-DILI in the training and test sets using the multiple logistic regression model was 87.62% and 88.27%, respectively, and the total accuracy of using CNN model was 92.36% and 91.70%, respectively. The sensitivity, specificity, and AUC of the CNN model were all higher than those of the multiple logistic regression model, and the differences were statistically significant (all P<0.05). Conclusion:Both the multiple logistic regression model and CNN model have good predictive performance for the occurrence of ATB-DILI, and the prediction performance of CNN model is better, comparatively.
