Department of Nursing Science, Faculty of Medicine, Nantong University, Nantong, 0086-226001, Jiangsu Province, China.
BMC Psychiatry. 2021 Oct 20;21(1):517. doi: 10.1186/s12888-021-03531-5.
Identifying important factors contributing to depression is necessary for interrupting risk pathways to minimize adolescent depression. The study aimed to assess the prevalence of depression in high school students and develop a model for identifying risk of depression among adolescents.
Cross-sectional study was conducted. A total of 1190 adolescents from two high schools in eastern China participated in the study. Artificial neurol network (ANN) was used to establish the identification model.
The prevalence of depression was 29.9% among the students. The model showed the top five protective and risk factors including perceived stress, life events, optimism, self-compassion and resilience. ANN model accuracy was 81.06%, with sensitivity 65.3%, specificity 88.4%, and area under the receiver operating characteristic (ROC) curves 0.846 in testing dataset.
The ANN showed the good performance in identifying risk of depression. Promoting the protective factors and reducing the level of risk factors facilitate preventing and relieving depression.
确定导致抑郁的重要因素对于阻断风险途径以最大程度地减少青少年抑郁至关重要。本研究旨在评估高中生抑郁的发生率，并为青少年抑郁风险识别建立模型。
采用横断面研究，选取中国东部两所高中的 1190 名青少年参与研究。采用人工神经网络（ANN）建立识别模型。
学生中抑郁的发生率为 29.9%。该模型显示了前五大保护和风险因素，包括感知压力、生活事件、乐观、自我同情和韧性。ANN 模型在测试数据集的准确率为 81.06%，灵敏度为 65.3%，特异性为 88.4%，ROC 曲线下面积为 0.846。
ANN 在识别抑郁风险方面表现出良好的性能。促进保护因素和降低风险因素水平有助于预防和缓解抑郁。
