Optimal survival trees
Dimitris Bertsimas, Jack Dunn, Emma Gibson, Agni Orfanoudaki
Abstract
Abstract Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these models to allow for the analysis of censored outcomes, which often appear in medical data. We present a new Optimal Survival Trees algorithm that leverages mixed-integer optimization (MIO) and local search techniques to generate globally optimized survival tree models. We demonstrate that the OST algorithm improves on the accuracy of existing survival tree methods, particularly in large datasets.
Topics & Concepts
Tree (set theory)Computer scienceScope (computer science)Survival analysisParametric statisticsMachine learningData miningArtificial intelligenceMathematicsStatisticsMathematical analysisProgramming languageStatistical Methods and InferenceMachine Learning in HealthcareExplainable Artificial Intelligence (XAI)