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In this study, we introduce a novel representation of patient data called Disease Severity Hierarchy (DSH) that explores specific diseases and their known treatment pathways in a nested fashion to create subpopulations in a clinically meaningful way. As the DSH tree is traversed from the root towards the leaves, we encounter subpopulations that share increasing richer amounts of clinical details such as similar disease severity, illness trajectories, and time to event that are discriminative, and suitable for learning risk stratification models. The proposed DSH risk scores effectively and accurately predict the age at which a patient may be at risk of dying or developing MCE significantly better than a traditional representation of disease conditions. DSH utilizes known relationships among various entities in EHR data to capture disease severity in a natural way and has the additional benefit of being expressive and interpretable. This novel patient representation can help support critical decision making, development of smart EBP guidelines, and enhance healthcare care and disease management by helping to identify and reduce disease burden among high-risk patients.