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We aim to bridge the gap between the machine learning community and the existing applications to healthcare. Our work involves developing efficient algorithms and probabilistic models using real-world data and expert knowledge. We employ state-of-the-art optimization techniques to understand the progression of disease symptoms and comorbidities over time. So far we have focused on customized probabilistic models in the context of Postpartum depression (PPD), cardiovascular disease, Alzheimer’s disease, and Parkinson’s disease; but our method is generalizable to summarizing patients with greater exactness to allow us to move toward personalized disease management strategies.