title: Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning.
abstract: To help cancer registrars efficiently and accurately identify reportable cancer cases.
  
  
  
  The Cancer Registry Control Panel (CRCP) was developed to detect mentions of reportable cancer cases using a pipeline built on the Unstructured Information Management Architecture - Asynchronous Scaleout (UIMA-AS) architecture containing the National Library of Medicine's UIMA MetaMap annotator as well as a variety of rule-based UIMA annotators that primarily act to filter out concepts referring to nonreportable cancers. CRCP inspects pathology reports nightly to identify pathology records containing relevant cancer concepts and combines this with diagnosis codes from the Clinical Electronic Data Warehouse to identify candidate cancer patients using supervised machine learning. Cancer mentions are highlighted in all candidate clinical notes and then sorted in CRCP's web interface for faster validation by cancer registrars.
  
  
  
  CRCP achieved an accuracy of 0.872 and detected reportable cancer cases with a precision of 0.843 and a recall of 0.848. CRCP increases throughput by 22.6% over a baseline (manual review) pathology report inspection system while achieving a higher precision and recall. Depending on registrar time constraints, CRCP can increase recall to 0.939 at the expense of precision by incorporating a data source information feature.
  
  
  
  CRCP demonstrates accurate results when applying natural language processing features to the problem of detecting patients with cases of reportable cancer from clinical notes. We show that implementing only a portion of cancer reporting rules in the form of regular expressions is sufficient to increase the precision, recall, and speed of the detection of reportable cancer cases when combined with off-the-shelf information extraction software and machine learning.
authors.affiliation: Center for Clinical and Translational Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294 ozborn@uab.edu.
authors.forename: John D
authors.initials: JD
authors.lastname: Osborne
authors.affiliation: Center for Clinical and Translational Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
authors.forename: Matthew
authors.initials: M
authors.lastname: Wyatt
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
authors.forename: Andrew O
authors.initials: AO
authors.lastname: Westfall
authors.affiliation: Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
authors.forename: James
authors.initials: J
authors.lastname: Willig
authors.affiliation: Department of Computer and Information Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
authors.forename: Steven
authors.initials: S
authors.lastname: Bethard
authors.affiliation: Informatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
authors.forename: Geoff
authors.initials: G
authors.lastname: Gordon
mesh_terms: D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D006801:Humans; D038801:International Classification of Diseases; D000069550:Machine Learning; D019221:Mandatory Reporting; D009323:Natural Language Processing; D009369:Neoplasms / Q000473:pathology; D010338:Pathology, Clinical; D014481:United States
keywords: electronic health records; information extraction; machine learning; natural language processing; neoplasms; user-computer interface