title: Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer.
abstract: BACKGROUND
  As many as 3% of computed tomography (CT) scans detect pancreatic cysts. Because pancreatic cysts are incidental, ubiquitous and poorly understood, follow-up is often not performed. Pancreatic cysts may have a significant malignant potential and their identification represents a 'window of opportunity' for the early detection of pancreatic cancer. The purpose of this study was to implement an automated Natural Language Processing (NLP)-based pancreatic cyst identification system.
  
  
  METHODS
  A multidisciplinary team was assembled. NLP-based identification algorithms were developed based on key words commonly used by physicians to describe pancreatic cysts and programmed for automated search of electronic medical records. A pilot study was conducted prospectively in a single institution.
  
  
  RESULTS
  From March to September 2013, 566,233 reports belonging to 50,669 patients were analysed. The mean number of patients reported with a pancreatic cyst was 88/month (range 78-98). The mean sensitivity and specificity were 99.9% and 98.8%, respectively.
  
  
  CONCLUSIONS
  NLP is an effective tool to automatically identify patients with pancreatic cysts based on electronic medical records (EMR). This highly accurate system can help capture patients 'at-risk' of pancreatic cancer in a registry.
authors.affiliation: Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Alexandra M
authors.initials: AM
authors.lastname: Roch
authors.forename: Saeed
authors.initials: S
authors.lastname: Mehrabi
authors.forename: Anand
authors.initials: A
authors.lastname: Krishnan
authors.forename: Heidi E
authors.initials: HE
authors.lastname: Schmidt
authors.forename: Joseph
authors.initials: J
authors.lastname: Kesterson
authors.forename: Chris
authors.initials: C
authors.lastname: Beesley
authors.forename: Paul R
authors.initials: PR
authors.lastname: Dexter
authors.forename: Mathew
authors.initials: M
authors.lastname: Palakal
authors.forename: C Max
authors.initials: CM
authors.lastname: Schmidt
mesh_terms: D000465:Algorithms; D001331:Automation; D055088:Early Detection of Cancer / Q000379:methods*; D005500:Follow-Up Studies; D006801:Humans; D009323:Natural Language Processing; D010181:Pancreatic Cyst / Q000175:diagnosis*; D010190:Pancreatic Neoplasms / Q000175:diagnosis*; D010865:Pilot Projects; D015203:Reproducibility of Results; D012189:Retrospective Studies