Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer

作者：Roch Alexandra M; Mehrabi Saeed; Krishnan Anand; Schmidt Heidi E; Kesterson Joseph; Beesley Chris; Dexter Paul R; Palakal Mathew; Schmidt C Max^*

来源：Hepato Pancreato Biliary, 2015, 17(5): 447-453.

DOI：10.1111/hpb.12375

IntroductionAs 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.
MethodA 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.
ResultsFrom March to September 2013, 566233 reports belonging to 50669 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.
ConclusionNLP 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.

• 出版日期2015-5