Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach.

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.

Methods

We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between 2016 and 2020. Symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, the Hospital Anxi

SUBMITTER: Hofman A 

PROVIDER: S-EPMC10044296 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature