When: February 21, 2017, 2:00 PM
Location: 3rd Floor Orchard View Room , Discovery Building
Contact: 608-316-4401, firstname.lastname@example.org
Mathematical Classification of Bipolar Disorder
Bipolar disorder is a chronic disease of mood instability. Longitudinal patterns of mood are central to any patient description, but are condensed into simple attributes and categories. Although these provide a common language for clinicians, they are not supported by empirical evidence. In this talk, I present patient-specific models of mood in bipolar disorder that incorporate existing longitudinal data. In the first part, I will describe mood as a Bayesian nonparametric hierarchical model that includes latent classes and patient-specific mood dynamics given by discrete-time Markov chains. These models are fit to weekly mood data, revealing three patient classes that differ significantly in attempted suicide rates, disability, and symptom chronicity. In the second part of the talk, I discuss how combined statistical inferences from a population do not support widely held assumptions (e.g. mood is one-dimensional, rhythmic, and/or multistable). I then present a stochastic differential equation model that does not make any of these assumptions. I show that this model accurately describes the data and that it can be personalized to an individual. Taken together, this work moves forward data-driven modeling approaches that can guide future research into precise clinical care and disease causes.
Bio: My main interest is Computational Psychiatry, an emerging field that aims to place subjective beliefs and behavior within a mathematical framework. Currently, I propose and verify computational phenotypes of major mood disorders. My professional goal is to demonstrate these phenotypes more closely reflect underlying pathological processes, thereby strengthening subsequent inferences from clinical and biological studies.
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