Temporal multi-step predictive modeling of remission in major depressive disorder using early stage treatment data; STAR*D based machine learning approach.

ABSTRACT:

Background

Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes.

Methods

Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step.

Results

Our model showed significant prediction performance across the three treatment steps, At step

SUBMITTER: Salem H 

PROVIDER: S-EPMC9863277 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature