Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings.

ABSTRACT: Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, o

SUBMITTER: Athreya AP 

PROVIDER: S-EPMC8134509 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature