Enkhbayar Doljinsuren, Ko Jaehoon, Oh Somin, Ferdushi Rumana, Kim Jaesoo, Key Jaehong, Urtnasan Erdenebayar
Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.
Division of Semiconductor System Engineering, Yonsei University, Wonju 26493, Republic of Korea.
Bioengineering (Basel). 2025 Feb 15;12(2):186. doi: 10.3390/bioengineering12020186.
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.
抑郁症是一种常见的心理健康障碍，也是导致死亡率和发病率的主要因素。尽管取得了一些进展，但目前的筛查方法在实现对抑郁症的强大且自动化检测方面存在局限性，从而阻碍了早期诊断和及时干预。本研究旨在开发可解释的人工智能（AI）模型，利用多导睡眠图表型数据预测抑郁症，确保具有高预测性能，同时清晰洞察影响抑郁症风险的特征的重要性。采用随机森林、极端梯度提升、分类提升和轻梯度提升机等先进机器学习算法来训练和验证预测性AI模型。对来自主观健康问卷、临床评估和人口统计学因素的表型数据进行了分析。可解释的AI模型识别出重要特征，并使用交叉验证评估其性能。根据经过验证的抑郁评分方法，对由114名对照参与者和39名抑郁症患者组成的研究人群进行了分层。所提出的可解释AI模型的F1分数达到85%，验证了其在预测抑郁症方面的高可靠性。影响抑郁症风险的关键特征，如焦虑症、睡眠效率和人口统计学因素，为临床实践提供了可采取行动的见解，突出了这些模型的透明度。本研究基于多导睡眠图表型数据提出并开发了用于自动检测抑郁症的可解释AI模型，并验证了这些模型有助于改善心理健康诊断，实现及时干预。
