title: Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.
abstract: BACKGROUND
  Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often "hidden" within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients.
  
  
  METHODS
  A corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents.
  
  
  RESULTS
  When evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors.
  
  
  CONCLUSIONS
  We describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.
authors: National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, Manchester, UK.
authors: Xiao
authors: X
authors: Fu
authors: National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, Manchester, UK ; Department of Computer Science, University of the Philippines Diliman, Quezon City, 1101 Philippines.
authors: Riza
authors: R
authors: Batista-Navarro
authors: National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, Manchester, UK.
authors: Rafal
authors: R
authors: Rak
authors: National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester Institute of Biotechnology, 131 Princess Street, Manchester, UK.
authors: Sophia
authors: S
authors: Ananiadou
keywords: Automatic annotation workflows; Chronic obstructive pulmonary disease; Corpora for clinical text mining; Corpus annotation; Ontology linking; Phenotype curation