Han Jiatong, Li Hao, Lin Han, Wu Pingping, Wang Shidan, Tu Juan, Lu Jing
School of Computer Science, Nanjing Audit University, China.
Jiangsu Key Laboratory of Public Project Audit, School of Engineering Audit, Nanjing Audit University, China.
Heliyon. 2023 Oct 5;9(10):e20684. doi: 10.1016/j.heliyon.2023.e20684. eCollection 2023 Oct.
Depression has become a widespread health concern today. Understanding the influencing factors can promote human mental health as well as provide a basis for exploring preventive measures. Combining LassoNet with recurrent neural network (RNN), this study constructed a screening model ,LassoNet-RNN, for identifying influencing factors of individual depression. Based on multi-wave surveys of China Health and Retirement Longitudinal Study (CHARLS) dataset (11,661 observations), we analyzed the multivariate time series data and recognized 27 characteristic variables selected from four perspectives: demographics, health-related risk factors, household economic status, and living environment. Additionally, the importance rankings of the characteristic variables were obtained. These results offered insightful recommendations for theoretical developments and practical decision making in public health.
抑郁症如今已成为一个广泛的健康问题。了解其影响因素有助于促进人类心理健康，并为探索预防措施提供依据。本研究将套索网络（LassoNet）与循环神经网络（RNN）相结合，构建了一个用于识别个体抑郁症影响因素的筛查模型LassoNet-RNN。基于中国健康与养老追踪调查（CHARLS）数据集的多轮调查（11661个观测值），我们分析了多变量时间序列数据，并识别出从人口统计学、健康相关风险因素、家庭经济状况和生活环境四个维度选取的27个特征变量。此外，还获得了特征变量的重要性排名。这些结果为公共卫生领域的理论发展和实际决策提供了有见地的建议。
