Data-Driven Longitudinal Modeling and Prediction of Symptom Dynamics in Major Depressive Disorder: Integrating Factor Graphs and Learning Methods

Arjun Athreya, Subho S. Banerjee, Drew Neavin, Rima Kaddurah-Daouk, A. John Rush, Mark A. Frye, Liewei Wang, Richard M. Weinshilboum, William V. Bobo and Ravishankar K. Iyer

CIBCB 2017

Best Paper Award

This paper proposes a data-driven longitudinal model that brings together factor graphs and learning methods to demonstrate a significant improvement in predictability in clinical outcomes of patients with major depressive disorder treated with antidepressants. Using data from the Mayo Clinic PGRN-AMPS trial and the STAR*D trial for validation, this work makes two significant contributions in the context of predictability in psychiatric therapeutic outcomes. First, we establish symptom dynamics in response to antidepressants by using the forward algorithm on a factor graph. Symptom dynamics are the changes in the symptom severity that are most likely to occur because of the antidepressants taken during the trial, and the associated clinical outcomes at 4 weeks and 8 weeks into the trial. The structure of the factor graph is inferred by using unsupervised learning to stratify patients by the similarity of their overall symptom severity. Second, by using metabolomics data as an accurate biological measure in addition to symptom survey data and other patient history information, the prediction of clinical outcomes such as response and remission significantly improved from 30% to 68% in men, and from 35% to 72% in women. This work demonstrates a significant difference in how men and women respond to antidepressants in terms of their symptom dynamics, and also shows that top predictors of clinical outcomes for men and women are significantly different and known to play a role in behavioral sciences.