Semi-supervised prediction of comorbid rare conditions using medical claims data

Abstract

Medical insurance claims data offer a coarse view of a patient's medical profile, including information about previous diagnoses and procedures performed. These data have been exploited in the past to predict presence of unmanifested conditions. Rarer conditions however, provide an extremely limited amount of ground truth to train supervised models, but predicting relevant co-morbidities can help reduce failure to rescue from a treatable, yet potentially life threatening condition. In this paper, we aim at a formidable task of improving models built to predict comorbidity of rare conditions that emerge during hospitalization and present PreCoRC, a novel approach that leverages hierarchical structures of diagnosis and procedure codes to alleviate the relatively low prevalence of specific types of Failure to Rescue (FTR) incidents. It can be applied post-hoc over previously learnt predictive models, and used to discover parts of the underlying hierarchies that contribute to the task. Our experimental results demonstrate that PreCoRC carries promise for operational utility in clinical settings, and offer insights into potential leading indicators of life threatening complications.