title: 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.
authors.forename: Katherine E
authors.initials: KE
authors.lastname: Niehaus
authors.forename: Holm H
authors.initials: HH
authors.lastname: Uhlig
authors.forename: David A
authors.initials: DA
authors.lastname: Clifton
mesh_terms: D002097:C-Reactive Protein / Q000378:metabolism; D016000:Cluster Analysis; D003424:Crohn Disease / Q000175:diagnosis*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008214:Lymphocytes / Q000378:metabolism; D008297:Male; D008962:Models, Theoretical; D010641:Phenotype; D012680:Sensitivity and Specificity; D012709:Serum Albumin / Q000378:metabolism
chemical_list: D012709:Serum Albumin; D002097:C-Reactive Protein