  Analysis of longitudinal Electronic Health Record (EHR) data is an important
goal for precision medicine. Difficulty in applying Machine Learning (ML)
methods, either predictive or unsupervised, stems in part from the
heterogeneity and irregular sampling of EHR data. We present an unsupervised
probabilistic model that captures nonlinear relationships between variables
over continuous-time. This method works with arbitrary sampling patterns and
captures the joint probability distribution between variable measurements and
the time intervals between them. Inference algorithms are derived that can be
used to evaluate the likelihood of future using under a trained model. As an
example, we consider data from the United States Veterans Health Administration
(VHA) in the areas of diabetes and depression. Likelihood ratio maps are
produced showing the likelihood of risk for moderate-severe vs minimal
depression as measured by the Patient Health Questionnaire-9 (PHQ-9).
