In-patient hospital data presents unique challenges for time series analysis, including the sparsity and irregularity of observations for each patient and the heterogeneous patient responses to interventions. In this talk, I will present a multi-output Gaussian process regression model for patient time series data that captures the state of a patient and uncertainty in this state across four vital signs and 20 lab tests in a patient-specific way. We build on top of this model a reinforcement learning approach to assist doctors to wean patients from a mechanical ventilator. Finally, I show how prior work with time series associations may be used with these data to identify patients with genetically-mediated responses to specific interventions. I will conclude with directions for future work.