The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution. However, prediction of disease progression with EHR is challenging since these data are sparse, heterogeneous, multi-dimensional, and multimodal time-series. Clustering is an alternative approach to identify similar groups within thepatient cohort that can be leveraged to predict patient status evolution. In particular, the identification of phenotypically separable clusters has proven very useful in improving healthcare delivery. Some clustering models have been proposed to identify phenotypically separable clusters; however, these have struggled in clinical settings characterised by several, highly imbalanced event classes. Tothat end, we propose a generative model to cluster EHR data based on identifying clinically meaningful phenotypes with regard to patient outcome prediction and physiological trajectory. We introduce a novel probabilistic method that is capable of simultaneously a) generating observation data, b) modelling temporal cluster assignments, and c) predicting admission outcomes. Our results show performancesimilar to state-of-the-art methods, with increased clustering separability, and capability to generate observation data.