Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder.

ABSTRACT: Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance personalized suicide control efforts. This study used unstructured psychiatric charts and brain magnetic resonance imaging (MRI) records from a psychiatric outpatient clinic to develop a machine learning-based suicidal thought classification model. The study included 152 patients with new depressive episodes for development and 58 patients from a geographically different hospital for validation. We developed an eXtreme Gradient Boosting (XGBoost)-based classification models according to the combined types of data: independent components-map weightin

SUBMITTER: Lee DY 

PROVIDER: S-EPMC11224278 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature