title: Linked data and online classifications to organise mined patterns in patient data.
abstract: In this paper, we investigate the use of web data resources in medicine, especially through medical classifications made available using the principles of Linked Data, to support the interpretation of patterns mined from patient care trajectories. Interpreting such patterns is naturally a challenge for an analyst, as it requires going through large amounts of results and access to sufficient background knowledge. We employ linked data, especially as exposed through the BioPortal system, to create a navigation structure within the patterns obtained form sequential pattern mining. We show how this approach provides a flexible way to explore data about trajectories of diagnoses and treatments according to different medical classifications.
authors.affiliation: Université de Lorraine, LORIA, UMR 7503Vandoevre-lès-Nancy, F-54506, France ; CHU de Nancy Nancy, F-54000, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Jay
authors.affiliation: Knowledge Media Institute, The Open UniversityWalton Hall, Milton Keynes, MK7 6AA, UK.
authors.forename: Mathieu
authors.initials: M
authors.lastname: d'Aquin
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D006760:Hospitalization; D006801:Humans; D020407:Internet; D010363:Pattern Recognition, Automated