Speaker
Description
Pain episodes are a defining feature of sickle cell disease and can lead to reduced quality of life. Statistical analyses by Valrie et al. (2021) examined interactions between physiological (like pain severity and sleep quality) and psychosocial variables (like positive and negative affect) in pediatric sickle cell patients. Clinical datasets can present challenges for continuous dynamical systems modeling as there may be a mix of continuous data and categorical data (for instance, whether a patient took a nap, disease genotype, or the type of medication taken) recorded within the dataset, and there may not be obvious biophysical relationships to guide modeling the variables. In this work-in-progress talk, we consider probabilistic graphical models (or Bayesian networks) which are flexible enough to address the mix of categorical and continuous data types present in a clinical dataset. We will explore the learned model structures between variables for subpopulations of patients defined by age.
