title: A methodology for mining clinical data: experiences from TRANSFoRm project.
abstract: Data mining of electronic health records (eHRs) allows us to identify patterns of patient data that characterize diseases and their progress and learn best practices for treatment and diagnosis. Clinical Prediction Rules (CPRs) are a form of clinical evidence that quantifies the contribution of different clinical data to a particular clinical outcome and help clinicians to decide the diagnosis, prognosis or therapeutic conduct for any given patient. The TRANSFoRm diagnostic support system (DSS) is based on the construction of an ontological repository of CPRs for diagnosis prediction in which clinical evidence is expressed using a unified vocabulary. This paper explains the proposed methodology for constructing this CPR repository, addressing algorithms and quality measures for filtering relevant rules. Some preliminary application results are also presented.
authors.affiliation: Imperial College London, London, UK.
authors.forename: Roxana
authors.initials: R
authors.lastname: Danger
authors.affiliation: Royal College of Surgeons in Ireland, Dublin, Ireland.
authors.forename: Derek
authors.initials: D
authors.lastname: Corrigan
authors.affiliation: Mediterranean Institute for Primary Care, Attard, Malta.
authors.forename: Jean K
authors.initials: JK
authors.lastname: Soler
authors.affiliation: Wroclaw University of Technology, Wroclaw, Poland.
authors.forename: Przemyslaw
authors.initials: P
authors.lastname: Kazienko
authors.affiliation: Wroclaw University of Technology, Wroclaw, Poland.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Kajdanowicz
authors.affiliation: Imperial College London, London, UK.
authors.forename: Azeem
authors.initials: A
authors.lastname: Majeed
authors.affiliation: King's College London, London, UK.
authors.forename: Vasa
authors.initials: V
authors.lastname: Curcin
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration; D057286:Electronic Health Records / Q000458:organization & administration*; D000069550:Machine Learning; D009323:Natural Language Processing