Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer. Issue 5 (May 2015)

• Record Type: Journal Article
• Authors: Roch, Alexandra M.; Mehrabi, Saeed; Krishnan, Anand; Schmidt, Heidi E.; Kesterson, Joseph; Beesley, Chris; Dexter, Paul R.; Palakal, Mathew; Schmidt, C. Max
• Is Part Of: HPB. Volume 17:Issue 5(2015:May)
• Page Start: 447
• Page End: 453
• Publication Date: 2015-05
• Subjects: Liver -- Diseases -- Periodicals; Biliary tract -- Diseases -- Periodicals; Pancreas -- Diseases -- Periodicals; 616.362005
• DOI: 10.1111/hpb.12375
• Languages: English
• ISSNs: 1365-182X

Abstract

Abstract: Introduction: As many as 3% of computed tomography (CT) scans detect pancreatic cysts. Because pancreatic cysts are incidental, ubiquitous and poorly understood, followup 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. Method: A multidisciplinary team was assembled. NLPbased 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. Conclusion: 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 'atrisk' of pancreatic cancer in a registry.