Speaker
Description
Mental disorders are a major contributor to the global burden of disease and often manifest as overlapping symptom profiles rather than isolated diagnoses. In clinical and digital health settings, this requires simultaneous assessment of multiple psychiatric domains, including depression, anxiety, trauma-related symptoms, substance use, and suicidality. However, current screening frameworks rely on separate disorder-specific questionnaires, producing lengthy assessments that increase response burden and limit scalability for large-scale or longitudinal monitoring. From a systems perspective, this raises the question of whether multi-disorder psychiatric risk signals are distributed across many questionnaire items or concentrated within a smaller set of core symptom dimensions. Here, we investigate structural compression in multi-disorder psychiatric assessment by identifying representative symptoms across six commonly used instruments. Our framework compresses 57 questionnaire items into a six-item representation while preserving strong predictive performance across all disorders (AUROC ≈ 0.90–0.97). Network analysis further shows that the selected symptoms occupy central positions within the symptom interaction network, suggesting that multi-condition psychiatric risk can be captured through a compact set of symptom dimensions.
Bibliography
[1] Grotzinger, A.D., Werme, J., Peyrot, W.J. et al. Mapping the genetic landscape across 14 psychiatric disorders. Nature 649, 406–415 (2026).
[2] Kotov, Roman, et al. "The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies." Journal of abnormal psychology 126.4 (2017): 454.
