Roch, Alexandra M.Mehrabi, SaeedKrishnan, AnandSchmidt, Heidi E.Kesterson, JosephBeesley, ChrisDexter, Paul R.Palakal, MatthewSchmidt, C. Max2017-07-132017-07-132015-05Roch, A. M., Mehrabi, S., Krishnan, A., Schmidt, H. E., Kesterson, J., Beesley, C., … Schmidt, C. M. (2015). Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer. HPB : The Official Journal of the International Hepato Pancreato Biliary Association, 17(5), 447–453. http://doi.org/10.1111/hpb.12375https://hdl.handle.net/1805/13435INTRODUCTION: 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. METHOD: 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. 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 'at-risk' of pancreatic cancer in a registry.en-USPublisher PolicyAlgorithmsAutomationEarly detection of cancer-- MethodsFollow-up studiesNatural language processingPancreatic cyst -- DiagnosisPancreatic neoplasms -- DiagnosisPilot projectsReproducibility of resultsRetrospective studiesArticle