Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach.
Journal:
European psychiatry : the journal of the Association of European Psychiatrists
PMID:
36804948
Abstract
BACKGROUND: Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement in a population-based sample across three of the most common categories of psychiatric disorders: depressive disorders, anxiety disorders, and sleep-wake disorders using unsupervised machine learning approaches.