Construction of diagnostic model of depression in insomnia patients based on polysomnography data
10.3969/j.issn.1002-0152.2024.11.004
- VernacularTitle:利用多导睡眠监测构建失眠患者抑郁症诊断模型
- Author:
Ning CAO
1
;
Huiru ZHANG
;
Liwei NIU
;
Rui ZHAO
Author Information
1. 内蒙古医科大学公共卫生学院,呼和浩特 010110
- Publication Type:Journal Article
- Keywords:
Insomnia;
Depression;
Polysomnography;
Machine learning;
Diagnostic model;
Logistic regression;
Random forest model
- From:
Chinese Journal of Nervous and Mental Diseases
2024;50(11):661-667
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a diagnostic model for depression in insomnia patients by mining polysomnography (PSG) data of insomnia patients with machine learning algorithms,and to provide a scientific basis for the diagnosis of depression in insomnia patients. Methods According to the inclusion and exclusion criteria,2162 insomnia inpatients and outpatients who attended the Inner Mongolia Autonomous Region Mental Health Center from January to December 2023 and underwent polysomnographic monitoring were included,and depression was diagnosed using the International Statistical Classification of Diseases and Related Health Problems,10th version (ICD-10). The general condition and PSG data of the patients were collected. Six algorithms—logistic regression (LR),Support vector machines (SVM),Random forest (RF),Adaptive Boosting (AdaBoost),Extreme Gradient Boosting (XGBoost) and Naive Bayes (NB)—were used to build the diagnostic model of depression in insomnia patients after the patients' general condition and PSG data were gathered. Results Among the enrolled patients with insomnia,40.1％ had comorbid depression. Among the six models,LR and RF exhibited the highest values of area under the curve (AUC) of receiver operating characteristic (ROC),at 0.825 and 0.823,respectively,indicating superior overall classification performance. Conclusion Logistic regression and random forest modeling have good diagnostic efficacy in the population of insomniacs with depression.
