The client is looking to evaluate the feasibility of enhancing their clinical decision support platform with additional functionality currently being described as “patients like mine”. Given a patients diagnostic history, attempt to identify other patients with similar features. This would allow the clinical decision support system to either automatically recommend follow-up treatment based on the history of similar patients, or to allow the clinician to review the records of similar patients in order to decide on the best next treatment. The objectives for the project included evaluation of unsupervised clustering algorithms which can be used to perform patient matching based on existing attributes. In addition, an R based workflow was created to perform feature engineering and model development. Finally, the goal was to identify one or more models which could be used to predict potential patient matches.