Construction of a predictive model for the efficacy of SNRI antidepressants in inpatients with moderate and severe depression based on machine learning

LIU Xuetao; LIU Yang; LI Hongjian; WU Jianhua; LIU Siming; JIAO Min; YU Luhai

doi:10.6039/j.issn.1001-0408.2025.15.21

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Construction of a predictive model for the efficacy of SNRI antidepressants in inpatients with moderate and severe depression based on machine learning

更新时间：2025-11-14

- Construction of a predictive model for the efficacy of SNRI antidepressants in inpatients with moderate and severe depression based on machine learning
- ZHONGGUO YAOFANG Vol. 36, Issue 15, Pages: 1936-1941(2025)
- 作者机构：
  
  1.石河子大学药学院，新疆石河子　832000
  2.新疆维吾尔自治区人民医院药学部，乌鲁木齐　830001
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2025.15.21
  CLC： R971+.43;TP181
- Received：10 February 2025，
  
  Revised：2025-07-31，
  
  Accepted：31 July 2025，
  
  Published：15 August 2025
- 稿件说明：
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刘学涛,刘阳,李红建,等.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[J].中国药房,2025,36(15):1936-1941.

LIU Xuetao,LIU Yang,LI Hongjian,et al.Construction of a predictive model for the efficacy of SNRI antidepressants in inpatients with moderate and severe depression based on machine learning[J].ZHONGGUO YAOFANG,2025,36(15):1936-1941.
刘学涛,刘阳,李红建,等.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[J].中国药房,2025,36(15):1936-1941. DOI： 10.6039/j.issn.1001-0408.2025.15.21.

LIU Xuetao,LIU Yang,LI Hongjian,et al.Construction of a predictive model for the efficacy of SNRI antidepressants in inpatients with moderate and severe depression based on machine learning[J].ZHONGGUO YAOFANG,2025,36(15):1936-1941. DOI： 10.6039/j.issn.1001-0408.2025.15.21.

摘要

目的

运用机器学习方法构建中重度抑郁症住院患者使用5-羟色胺去甲肾上腺素再摄取抑制剂（SNRI）的疗效预测模型。

方法

回顾性收集2022年1月至2024年10月在新疆某三甲医院使用SNRI类药物治疗的中重度抑郁症住院患者病历资料，根据24项汉密尔顿抑郁量表评分标准的减分率，将患者分为有效组与无效组；经过LASSO回归筛选与SNRI类药物疗效相关的特征变量，应用训练集构建支持向量机、k近邻、随机森林、轻量级梯度提升机和极端梯度提升5种预测模型，使用贝叶斯优化算法调整模型的超参数，再以验证集评估模型性能，以筛选出最优模型。应用夏普利加性解释方法对最优模型进行解释。

结果

共收集到355例中重度抑郁症住院患者的病历资料，其中有效组285例、无效组70例，治疗有效率为80.28%。经过特征变量筛选，得到与疗效相关的5个特征变量，分别为汉密尔顿焦虑量表评分、血尿素氮、合用抗焦虑药物、饮酒史、首次发病。与其他模型相比，随机森林模型的性能表现最优，其受试者工作特征曲线下面积值为0.85，精确率-召回率曲线下面积值为0.87，准确度为0.74，召回率为0.75。

结论

基于5种特征变量建立的随机森林模型可用于中重度抑郁症住院患者使用SNRI类药物的疗效预测。

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.

关键词

Keywords

references

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