title: De-identification of health records using Anonym: effectiveness and robustness across datasets.
abstract: OBJECTIVE
  Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness.
  
  
  METHODS
  The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors.
  
  
  RESULTS
  Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training.
  
  
  CONCLUSIONS
  Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.
authors.affiliation: The Australian e-Health Research Centre (Commonwealth Scientific and Industrial Research Organisation), Level 5 - UQ Health Sciences Building 901/16, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia; School of Information Systems, Queensland University of Technology, Y Block Level 6, Gardens Point Campus, Brisbane, QLD, Australia. Electronic address: g.zuccon@qut.edu.au.
authors.forename: Guido
authors.initials: G
authors.lastname: Zuccon
authors.affiliation: The Australian e-Health Research Centre (Commonwealth Scientific and Industrial Research Organisation), Level 5 - UQ Health Sciences Building 901/16, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia.
authors.forename: Daniel
authors.initials: D
authors.lastname: Kotzur
authors.affiliation: The Australian e-Health Research Centre (Commonwealth Scientific and Industrial Research Organisation), Level 5 - UQ Health Sciences Building 901/16, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia.
authors.forename: Anthony
authors.initials: A
authors.lastname: Nguyen
authors.affiliation: Cancer Institute NSW, Australian Technology Park, Level 9, 8 Central Avenue, Eveleigh, NSW 2015, Australia.
authors.forename: Anton
authors.initials: A
authors.lastname: Bergheim
mesh_terms: D001185:Artificial Intelligence; D003219:Confidentiality; D016208:Databases, Factual; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D020408:Health Insurance Portability and Accountability Act; D055991:Health Records, Personal; D006801:Humans; D010355:Patient Identification Systems; D012984:Software; D014481:United States
keywords: Conditional random fields; De-identification; Health records; Pattern matching