Abstract Treatment outcomes for major depressive disorder (MDD) need to be improved.
Presently, no clinically relevant tools have been established for stratifying subgroups or …

Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …

Background No previous study has comprehensively reviewed the application of machine
learning algorithms in mood disorders populations. Herein, we qualitatively and …

Background Many depressed patients are not able to achieve or sustain symptom remission
despite serial treatment trials–often termed “treatment resistant depression”. A broader …

Purpose The advent of clinically adapted machine learning algorithms can solve numerous
problems ranging from disease diagnosis and prognosis to therapy recommendations. This …

Background The impact of precision psychiatry for clinical practice has not been
systematically appraised. This study aims to provide a comprehensive review of validated …

Objectives: The overview outlines two decades of research from the European Group for the
Study of Resistant Depression (GSRD) that fundamentally impacted evidence-based …

Introduction Growing demand for mental health services, coupled with funding and resource
limitations, creates an opportunity for novel technological solutions including artificial …

Background To determine the incidence of Treatment-Resistant Depression (TRD) in Spain
and to estimate its economic burden, using real world data. Methods A retrospective …

Background Individuals with major depressive disorder (MDD) are at higher risk for obesity.
In turn, weight gain is a predisposing factor for depression. Although clinical data are sparse …