Construction of a predictive model for the efficacy of SNRI antidepressants in inpatients with moderate and severe depression based on machine learning
- VernacularTitle:基于机器学习构建中重度抑郁症住院患者使用SNRI类抗抑郁药的疗效预测模型
- Author:
Xuetao LIU
1
,
2
;
Yang LIU
1
;
Hongjian LI
2
;
Jianhua WU
2
;
Siming LIU
2
;
Ming JIAO
2
;
Luhai YU
2
Author Information
1. School of Pharmacy，Shihezi University，Xinjiang Shihezi 832000，China
2. Dept. of Pharmacy，Xinjiang Uygur Autonomous Region People’s Hospital，Urumqi 830001，China
- Publication Type:Journal Article
- Keywords:
serotonin-norepinephrine reuptake inhibitor
- From:
China Pharmacy
2025;36(15):1936-1941
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To construct a prediction model for the efficacy of serotonin-norepinephrine reuptake inhibitor （SNRI） in inpatients with moderate and severe depression by using a machine learning method. METHODS The case records of inpatients with moderate and severe depression treated with SNRI antidepressants were collected from a third-grade class-A hospital in Xinjiang from January 2022 to October 2024； those patients were divided into effective group and ineffective group based on the Hamilton depression scale-24 score reduction rate. After screening the characteristic variables related to the therapeutic efficacy of SNRI drugs through LASSO regression， five prediction models including support vector machine， k-nearest neighbor， random forest， lightweight gradient boosting machine and extreme gradient boosting were constructed using the training set. Bayesian optimization was used to adjust the hyperparameters of these models. The performance of the models was evaluated in the validation set to select the optimal model. The Shapley additive explanations method was used to perform explainable analysis on the best model. RESULTS The medical records from 355 hospitalized patients with moderate and severe depression were collected， comprising 285 cases in the effective group and 70 cases in the ineffective group， resulting in an overall therapeutic response rate of 80.28%. After feature variable screening， five characteristic variables for therapeutic efficacy were obtained， including Hamilton anxiety scale， blood urea nitrogen， combination of anti-anxiety drugs， drinking history， and first onset of the disease. Compared with other models， the random forest model performed the best. The area under the receiver operating characteristic curve was 0.85， the area under the precision-recall curve was 0.87， the accuracy was 0.74， and the recall rate value was 0.75. CONCLUSIONS The random forest model constructed based on five characteristic variables demonstrates potential for predicting the therapeutic efficacy of SNRI antidepressants in hospitalized patients with moderate and severe depression.
