论文标题
症状识别可解释的多种精神疾病
Symptom Identification for Interpretable Detection of Multiple Mental Disorders
论文作者
论文摘要
由于缺乏症状建模，社交媒体的心理疾病检测（MDD）的普遍性和可解释性差。本文介绍了PSYSYM，这是多种精神疾病的第一个注释症状识别语料库，以促进进一步的研究进展。根据与已建立的临床手册和尺度符合的7种精神疾病相关的38种症状类别的知识图，对PSYSYM进行注释，以及针对多样性和质量的新颖注释框架。实验表明，由psysym启用的症状辅助MDD可以胜过强纯文本基准。我们还展示了通过案例研究进行症状预测提供的令人信服的MDD解释，并指出了它们的进一步潜在应用。
Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the first annotated symptom identification corpus of multiple psychiatric disorders, to facilitate further research progress. PsySym is annotated according to a knowledge graph of the 38 symptom classes related to 7 mental diseases complied from established clinical manuals and scales, and a novel annotation framework for diversity and quality. Experiments show that symptom-assisted MDD enabled by PsySym can outperform strong pure-text baselines. We also exhibit the convincing MDD explanations provided by symptom predictions with case studies, and point to their further potential applications.
