Depression is a common disease worldwide. It is difficult to diagnose and continues to be underdiagnosed. Because depressed patients constantly share their symptoms, major life events, and treatments on social media, researchers are turning to user-generated digital traces on social media for depression detection. Such methods have distinct advantages in combating depression because they can facilitate innovative approaches to fight depression and alleviate its social and economic burden. However, most existing studies lack effective means to incorporate established medical domain knowledge in depression detection or suffer from feature extraction difficulties that impede greater performance. Following the design science research paradigm, we propose a Deep Knowledge-aware Depression Detection (DKDD) framework to accurately detect social media users at risk of depression and explain the critical factors that contribute to such detection. Extensive empirical studies with real-world data demonstrate that, by incorporating domain knowledge, our method outperforms existing state-of-the-art methods. Our work has significant implications for IS research in knowledge-aware machine learning, digital traces utilization, and NLP research in IS. Practically, by providing early detection and explaining the critical factors, DKDD can supplement clinical depression screening and enable large-scale evaluations of a population's mental health status.
翻译:抑郁症是全世界常见的疾病,很难诊断,并且继续得不到充分诊断。由于抑郁症患者经常在社交媒体上分享其症状、重大生活事件和治疗,研究人员正在借助社交媒体用户生成的数字痕迹来发现抑郁症。这些方法在防治抑郁症方面有着显著的优势,因为他们可以促进采用创新方法来克服抑郁症并减轻其社会和经济负担。然而,大多数现有研究缺乏有效的手段,无法将既定的医疗领域知识纳入抑郁症检测,或面临阻碍更大绩效的特质提取困难。根据设计科学研究模式,我们提议建立一个深知抑郁症检测框架,以准确发现有抑郁症风险的社会媒体用户,并解释促成这种检测的关键因素。用现实世界数据进行的广泛实证研究表明,通过纳入域知识,我们的方法超越了现有的最新方法。我们的工作对信息信息系统在知识意识机器学习、数字痕迹利用和IS的NLP研究具有重大影响。实际上,通过提供早期检测和解释关键因素,DDDD可以补充临床抑郁症筛查,并促成对人口心理健康状况的大规模评估。</s>