DOI: 10.1101/19006650Sep 18, 2019Paper

Predicting individual clinical trajectories of depression with generative embedding

MedRxiv : the Preprint Server for Health Sciences
S. FrässleKlaas E Stephan

Abstract

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift and enduring recovery, others show relapsing-remitting or chronic disease course. Predicting individual clinical trajectories at an early disease stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy - generative embedding - which combines an interpretable generative model with a discriminative classifier. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the multi-site longitudinal NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), it was possible to predict whether a given patient will experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remi...Continue Reading

Related Concepts

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Glasgow Medical Journal
William Mitchell Banks
Glasgow Medical Journal
William Mitchell Banks
PLoS Computational Biology
Kay H BrodersenKlaas E Stephan
© 2021 Meta ULC. All rights reserved