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Related Experiment Video

Updated: May 9, 2026

Robotic Duodenum-preserving Total Pancreatic Head Resection for Intraductal Papillary Mucinous Neoplasms
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Robotic Duodenum-preserving Total Pancreatic Head Resection for Intraductal Papillary Mucinous Neoplasms

Published on: April 17, 2026

An efficient pancreatic cyst identification methodology using natural language processing.

Saeed Mehrabi1, C Max Schmidt, Joshua A Waters

  • 1School of Informatics, Indiana University, Indianapolis, IN, USA.

Studies in Health Technology and Informatics
|August 8, 2013
PubMed
Summary
This summary is machine-generated.

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This study developed a Natural Language Processing (NLP) system to identify pancreatic cysts in patient records. The system improved early detection of pancreatic cancer risk, enhancing patient surveillance.

Area of Science:

  • Medical Informatics
  • Oncology
  • Natural Language Processing

Background:

  • Pancreatic cancer is a deadly disease often diagnosed late.
  • Patients with pancreatic cysts face an increased risk of developing pancreatic cancer.
  • Early detection through surveillance of pancreatic cysts can improve outcomes.

Purpose of the Study:

  • To develop and evaluate a Natural Language Processing (NLP) system for identifying pancreatic cysts in electronic health records.
  • To assess the performance of the NegEx algorithm and its enhancement using the Stanford Dependency Parser (SDP) for clinical concept extraction.

Main Methods:

  • Retrospective analysis of 1064 patient records from Indiana University Hospital (1990-2012).
  • Development of an NLP system incorporating the NegEx algorithm for negation detection.

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Last Updated: May 9, 2026

Robotic Duodenum-preserving Total Pancreatic Head Resection for Intraductal Papillary Mucinous Neoplasms
10:10

Robotic Duodenum-preserving Total Pancreatic Head Resection for Intraductal Papillary Mucinous Neoplasms

Published on: April 17, 2026

  • Application of the Stanford Dependency Parser (SDP) to refine negation identification and feature extraction.
  • Utilized regular expressions (regex) for extracting cyst-related features.

    • Main Results:

    • The initial NegEx algorithm achieved 98.9% precision and 89% recall for negation identification.
    • Integrating SDP improved NegEx recall to 95.7% for negation status.
    • Feature extraction using regex and NegEx yielded 98.5% precision and 97.43% recall.
    • SDP further enhanced the NegEx algorithm's recall for feature extraction to 98.12%.

    Conclusions:

    • NLP systems, particularly when enhanced with SDP, can effectively identify pancreatic cysts in clinical notes.
    • Improved identification of pancreatic cysts aids in early detection and surveillance for pancreatic cancer.
    • This approach demonstrates the potential for automated analysis of electronic health records to support cancer risk assessment.