.png){kind=icon}
返回
Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes
.png){kind=icon}
OA
AI
DOI：10.3390/bioengineering12020186.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Depression is a common mental health disorder and a leading contributor to mortality and morbidity. Despite several advancements, the current screening methods have limitations in enabling the robust and automated detection of depression, thereby hindering early diagnosis and timely intervention. This study aimed to develop explainable artificial intelligence (AI) models to predict depression using polysomnographic phenotype data, ensuring high predictive performance while providing clear insights into the importance of features influencing the risk of depression. Advanced machine learning algorithms such as random forest, extreme gradient boosting, categorical boosting, and light gradient boosting machines were employed to train and validate the predictive AI models. Phenotype data from subjective health questionnaires, clinical assessments, and demographic factors were analyzed. The explainable AI models identified the important features, and their performance was evaluated using cross-validation. The study population, comprising 114 control participants and 39 individuals with depression, was stratified based on validated depression-scoring methods. The proposed explainable AI models achieved an F1-score of 85%, verifying their high reliability in predicting depression. Key features influencing the risk of depression, such as anxiety disorders, sleep efficiency, and demographic factors, offer actionable insights for clinical practice, highlighting the transparency of these models. This study proposed and developed explainable AI models based on polysomnographic phenotype data for the automated detection of depression and verified that these models help improve mental health diagnostics, enabling timely interventions.
Keyword:
explainable artificial intelligence
machine learning
depression
polysomnographic phenotypes
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
B
IF:
3.7
论文数:
5.9K
被引数:
1.3W
机构
引用论文
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects集成学习综述: 概念、算法、应用与展望.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Suicide risk in people with post-traumatic stress disorder: A cohort study of 3.1 million people in Sweden创伤后应激障碍患者的自杀风险: 一项针对瑞典310万人的队列研究OAAI
OAAI分享收藏SciPy 1.0: fundamental algorithms for scientific computing in PythonSciPy 1.0: Python中科学计算的基本算法NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏分享收藏分享收藏
查看更多内容