Mental and physical disorders (MPD) are inextricably linked in many medical cases; psychosomatic diseases can be induced by mental concerns and psychological discomfort can ensue from physiological diseases. However, existing medical informatics studies focus on identifying mental or physical disorders from a unilateral perspective. Consequently, no existing domain knowledge base, corpus, or detection modeling approach considers mental as well as physical aspects concurrently. This paper proposes a joint modeling approach to detect MPD. First, we crawl through online medical consultation records of patients from websites and build an MPD knowledge ontology by extracting the core conceptual features of the text. Based on the ontology, an MPD knowledge graph containing 12,673 nodes and 82,195 relations is obtained using term matching with a domain thesaurus of each concept. Subsequently, an MPD corpus with fine-grained severities (, , , , ) and 8909 records is constructed by formulating MPD classification criteria and a data annotation process under the guidance of domain experts. Taking the knowledge graph and corpus as the dataset, we design a multi-task learning model to detect the MPD severity, in which a knowledge graph attention network (KGAT) is embedded to better extract knowledge features. Experiments are performed to demonstrate the effectiveness of our model. Furthermore, we employ ontology-based and centrality-based methods to discover additional potential inferred knowledge, which can be captured by KGAT so as to improve the prediction performance and interpretability of our model. Our dataset has been made publicly available, so it can be further used as a medical informatics reference in the fields of psychosomatic medicine, psychiatrics, physical co-morbidity, and so on.

中文翻译：

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使用配备知识图注意网络的多任务学习检测精神和身体疾病

在许多医疗案例中，精神和身体疾病 (MPD) 有着千丝万缕的联系；精神问题可能引发心身疾病，生理疾病可能引发心理不适。然而，现有的医学信息学研究侧重于从单方面的角度识别精神或身体疾病。因此，现有的领域知识库、语料库或检测建模方法都没有同时考虑心理和物理方面。本文提出了一种联合建模方法来检测 MPD。首先，我们从网站上爬取患者的在线医疗咨询记录，并通过提取文本的核心概念特征来构建 MPD 知识本体。基于本体，通过与每个概念的领域词库进行术语匹配，得到包含12,673个节点和82,195个关系的MPD知识图。随后，在领域专家的指导下，通过制定MPD分类标准和数据标注过程，构建了具有细粒度严重程度（、、、、、）和8909条记录的MPD语料库。以知识图和语料库为数据集，我们设计了一个多任务学习模型来检测MPD严重程度，其中嵌入了知识图注意网络（KGAT）以更好地提取知识特征。进行实验来证明我们模型的有效性。此外，我们采用基于本体和基于中心性的方法来发现额外的潜在推断知识，这些知识可以被 KGAT 捕获，从而提高我们模型的预测性能和可解释性。我们的数据集已经公开，因此可以进一步用作心身医学、精神病学、身体共病等领域的医学信息学参考。