论文标题
从文本数据中迈向基于知识的精神障碍模式采矿
Towards Knowledge-based Mining of Mental Disorder Patterns from Textual Data
论文作者
论文摘要
心理健康障碍可能会对所有国家的经济和健康造成严重后果。例如，孤立和旅行禁令等共vid-19-19大流行的影响会使我们感到沮丧。确定精神健康障碍的早期迹象至关重要。例如，抑郁症可能会增加个人自杀的风险。从文本数据中识别精神障碍模式的最先进的研究，使用手工标记的训练集，尤其是当需要域专家的知识来分析各种症状时。这项任务可能是耗时且昂贵的。为了应对这一挑战，在本文中，我们研究和分析了鉴定精神健康障碍的各种临床和非临床方法。我们利用认知科学的领域知识和专业知识来为精神健康障碍概念和模式建立特定领域的知识库（KB）。我们通过促进特定于领域的知识库（KB）的培训数据来提出较弱的监督形式。我们采用典型的方案来分析社交媒体，以识别社交用户产生的文本内容中的主要抑郁症症状。我们使用这种情况来评估我们的基于知识的方法如何显着提高结果质量。
Mental health disorders may cause severe consequences on all the countries' economies and health. For example, the impacts of the COVID-19 pandemic, such as isolation and travel ban, can make us feel depressed. Identifying early signs of mental health disorders is vital. For example, depression may increase an individual's risk of suicide. The state-of-the-art research in identifying mental disorder patterns from textual data, uses hand-labelled training sets, especially when a domain expert's knowledge is required to analyse various symptoms. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyse the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base (KB) for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating the generating of training data from a domain-specific Knowledge Base (KB). We adopt a typical scenario for analysing social media to identify major depressive disorder symptoms from the textual content generated by social users. We use this scenario to evaluate how our knowledge-based approach significantly improves the quality of results.
