Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine.

ABSTRACT:

Background

Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment.

Methods

We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV cli

SUBMITTER: Benoit JRA 

PROVIDER: S-EPMC8808003 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature