Phenotypic characterisation of Crohn's disease severity.

Abstract

Crohn's disease (CD) is a highly heterogeneous disease, with great variation in patient severity. Using supervised machine learning techniques to predict severity from common laboratory and clinical measurements, we found that high levels of C-reactive protein and low levels of lymphocytes and albumin are important predictive factors. Building upon this knowledge, we used extreme value theory to create a probabilistic model that combines information about behaviour in the extremes of these lab measurements to produce a single risk score over time. We then clustered these patient risk scores to identify several common clinical trajectories for CD patients.