title: Cross-hospital portability of information extraction of cancer staging information.
abstract: OBJECTIVE
  We address the task of extracting information from free-text pathology reports, focusing on staging information encoded by the TNM (tumour-node-metastases) and ACPS (Australian clinico-pathological stage) systems. Staging information is critical for diagnosing the extent of cancer in a patient and for planning individualised treatment. Extracting such information into more structured form saves time, improves reporting, and underpins the potential for automated decision support.
  
  
  METHODS
  We investigate the portability of a text mining model constructed from records from one health centre, by applying it directly to the extraction task over a set of records from a different health centre, with different reporting narrative characteristics. Other than a simple normalisation step on features associated with target labels, we apply the models from one system directly to the other.
  
  
  RESULTS
  The best F-scores for in-hospital experiments are 81%, 85%, and 94% (for staging T, N, and M respectively), while best cross-hospital F-scores reach 84%, 81%, and 91% for the same respective categories.
  
  
  CONCLUSIONS
  Our performance results compare favourably to the best levels reported in the literature, and--most relevant to our aim here--the cross-corpus results demonstrate the portability of the models we developed.
authors.affiliation: Department of Computing and Information Systems, The University of Melbourne, Doug McDonell Building, Parkville, 3010 VIC, Australia. Electronic address: david.martinez.iraola@gmail.com.
authors.forename: David
authors.initials: D
authors.lastname: Martinez
authors.affiliation: Barwon Health, Geelong Hospital, 1/75 Bellerine Street, Geelong, 3220 VIC, Australia.
authors.forename: Graham
authors.initials: G
authors.lastname: Pitson
authors.affiliation: Department of Computing and Information Systems, The University of Melbourne, Doug McDonell Building, Parkville, 3010 VIC, Australia.
authors.forename: Andrew
authors.initials: A
authors.lastname: MacKinlay
authors.affiliation: School of Computer Science and IT, RMIT University, 124 Latrobe St, Melbourne, 3000 VIC, Australia.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Cavedon
mesh_terms: D000465:Algorithms; D015179:Colorectal Neoplasms / Q000473:pathology*; D057225:Data Mining; D006751:Hospital Information Systems; D006801:Humans; D008499:Medical Records; D009323:Natural Language Processing; D009367:Neoplasm Staging
keywords: Cancer staging detection; Colorectal cancer; Information extraction; Machine learning; Text mining