Costello Francis Joseph, Kim Cheong, Kang Chang Min, Lee Kun Chang
SKK Business School, Sungkyunkwan University, Seoul 03063, Korea.
Airports Council International (ACI) World, Montreal, QC H4Z 1G8, Canada.
Healthcare (Basel). 2020 Dec 15;8(4):562. doi: 10.3390/healthcare8040562.
It has been reported repeatedly that depression in middle-aged people may cause serious ramifications in public health. However, previous studies on this important research topic have focused on utilizing either traditional statistical methods (i.e., logistic regressions) or black-or-gray artificial intelligence (AI) methods (i.e., neural network, Support Vector Machine (SVM), ensemble). Previous studies lack suggesting more decision-maker-friendly methods, which need to produce clear interpretable results with information on cause and effect. For the sake of improving the quality of decisions of healthcare decision-makers, public health issues require identification of cause and effect information for any type of strategic healthcare initiative. In this sense, this paper proposes a novel approach to identify the main causes of depression in middle-aged people in Korea. The proposed method is the Sons and Spouses Bayesian network model, which is an extended version of conventional TAN (Tree-Augmented Naive Bayesian Network). The target dataset is a longitudinal dataset employed from the Korea National Health and Nutrition Examination Survey (KNHANES) database with a sample size of 8580. After developing the proposed Sons and Spouses Bayesian network model, we found thirteen main causes leading to depression. Then, genetic optimization was executed to reveal the most probable cause of depression in middle-aged people that would provide practical implications to field practitioners. Therefore, our proposed method can help healthcare decision-makers comprehend changes in depression status by employing what-if queries towards a target individual.
已有反复报道称，中年人的抑郁症可能会对公共卫生造成严重影响。然而，此前关于这一重要研究课题的研究主要集中在使用传统统计方法（即逻辑回归）或非黑即白的人工智能（AI）方法（即神经网络、支持向量机（SVM）、集成方法）。此前的研究缺乏提出更便于决策者使用的方法，这些方法需要产生具有因果信息的清晰可解释结果。为了提高医疗保健决策者的决策质量，公共卫生问题需要为任何类型的战略性医疗保健举措确定因果信息。从这个意义上说，本文提出了一种新方法来识别韩国中年人群体中抑郁症的主要成因。所提出的方法是子女与配偶贝叶斯网络模型，它是传统树增强朴素贝叶斯网络（TAN）的扩展版本。目标数据集是从韩国国家健康与营养检查调查（KNHANES）数据库中采用的纵向数据集，样本量为8580。在开发了所提出的子女与配偶贝叶斯网络模型后，我们发现了导致抑郁症的13个主要成因。然后，进行了遗传优化，以揭示中年人群体中抑郁症最可能的成因，这将为现场从业者提供实际启示。因此，我们提出的方法可以帮助医疗保健决策者通过对目标个体进行假设查询来理解抑郁症状况的变化。
