Symptom Identification for Interpretable Detection of Multiple Mental Disorders
This work addresses interpretable detection of mental disorders for researchers and clinicians, but it is incremental as it focuses on creating a new dataset rather than a novel method.
The paper tackled the problem of poor generalizability and interpretability in mental disease detection from social media by introducing PsySym, the first annotated symptom identification corpus for multiple psychiatric disorders, which enabled symptom-assisted detection to outperform strong pure-text baselines.
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.