Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach.

ABSTRACT:

Background

Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.

Methods

In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs:

SUBMITTER: Bokma WA 

PROVIDER: S-EPMC8711102 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature