title: Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries.
abstract: BACKGROUND
  Clinical trial registries may allow for producing a global mapping of health research. However, health conditions are not described with standardized taxonomies in registries. Previous work analyzed clinical trial registries to improve the retrieval of relevant clinical trials for patients. However, no previous work has classified clinical trials across diseases using a standardized taxonomy allowing a comparison between global health research and global burden across diseases. We developed a knowledge-based classifier of health conditions studied in registered clinical trials towards categories of diseases and injuries from the Global Burden of Diseases (GBD) 2010 study. The classifier relies on the UMLS® knowledge source (Unified Medical Language System®) and on heuristic algorithms for parsing data. It maps trial records to a 28-class grouping of the GBD categories by automatically extracting UMLS concepts from text fields and by projecting concepts between medical terminologies. The classifier allows deriving pathways between the clinical trial record and candidate GBD categories using natural language processing and links between knowledge sources, and selects the relevant GBD classification based on rules of prioritization across the pathways found. We compared automatic and manual classifications for an external test set of 2,763 trials. We automatically classified 109,603 interventional trials registered before February 2014 at WHO ICTRP.
  
  
  RESULTS
  In the external test set, the classifier identified the exact GBD categories for 78 % of the trials. It had very good performance for most of the 28 categories, especially "Neoplasms" (sensitivity 97.4 %, specificity 97.5 %). The sensitivity was moderate for trials not relevant to any GBD category (53 %) and low for trials of injuries (16 %). For the 109,603 trials registered at WHO ICTRP, the classifier did not assign any GBD category to 20.5 % of trials while the most common GBD categories were "Neoplasms" (22.8 %) and "Diabetes" (8.9 %).
  
  
  CONCLUSIONS
  We developed and validated a knowledge-based classifier allowing for automatically identifying the diseases studied in registered trials by using the taxonomy from the GBD 2010 study. This tool is freely available to the research community and can be used for large-scale public health studies.
authors.affiliation: Centre d'Épidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France. ignacio.atal-ext@aphp.fr.|INSERM U1153, Paris, France. ignacio.atal-ext@aphp.fr.|Université Paris Descartes, Paris, France. ignacio.atal-ext@aphp.fr.
authors.forename: Ignacio
authors.initials: I
authors.lastname: Atal
authors.affiliation: Centre d'Épidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.|INSERM U1153, Paris, France.|Université Paris Descartes, Paris, France.
authors.forename: Jean-David
authors.initials: JD
authors.lastname: Zeitoun
authors.affiliation: LIMSI, CNRS UPR 3251, Université Paris-Saclay, Orsay, France.
authors.forename: Aurélie
authors.initials: A
authors.lastname: Névéol
authors.affiliation: Centre d'Épidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.|INSERM U1153, Paris, France.|Université Paris Descartes, Paris, France.|Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
authors.forename: Philippe
authors.initials: P
authors.lastname: Ravaud
authors.affiliation: Centre d'Épidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.|INSERM U1153, Paris, France.|Université Paris Descartes, Paris, France.
authors.forename: Raphaël
authors.initials: R
authors.lastname: Porcher
authors.affiliation: Centre d'Épidémiologie Clinique, Hôpital Hôtel-Dieu, Paris, France.|INSERM U1153, Paris, France.|Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
authors.forename: Ludovic
authors.initials: L
authors.lastname: Trinquart
keywords: Clinical trials; Disease classification; Global burden of diseases; Mapping