Title: OPERA: An Interpretable Algorithm for Patient Stratification based on Partially Ordered Risk Factors

Abstract: Risk stratification is an invaluable tool for modern healthcare systems. By separating patients into subgroups with distinct disease severity and prognosis, it allows better clinical decision making due to targeted care thus ultimately fosters healthier patient populations. In addition, it enhances communication, engagement, and research focus. This talk presents a new algorithm entitled ‘Ordering Poset Elements by Recursive Amalgamation’ (OPERA) for patient stratification using many risk factors. Health risk factors frequently exhibit total or partial ordering and when considered jointly, they form a partially ordered set or a poset. Compared with the usual regression models, OPERA can explore high order interactions similar to the well-known tree method. On the other hand, by explicitly exploring the poset structure, OPERA allows flexible and interpretable staging patterns and faster pruning for better performance. OPERA is evaluated in extensive simulation studies and cancer staging data to demonstrate its ability in performing risk stratification using ordered risk factors.