An efficient pancreatic cyst identification methodology using natural language processing

Saeed Mehrabi, C. Max Schmidt, Joshua A. Waters, Chris Beesley, Anand Krishnan, Joe Kesterson, Paul Dexter, Mohammed A. Al-Haddad, William M. Tierney, Mathew Palakal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Pancreatic cancer is one of the deadliest cancers, mostly diagnosed at late stages. Patients with pancreatic cysts are at higher risk of developing cancer and their surveillance can help to diagnose the disease in earlier stages. In this retrospective study we collected a corpus of 1064 records from 44 patients at Indiana University Hospital from 1990 to 2012. A Natural Language Processing (NLP) system was developed and used to identify patients with pancreatic cysts. NegEx algorithm was used initially to identify the negation status of concepts that resulted in precision and recall of 98.9% and 89% respectively. Stanford Dependency parser (SDP) was then used to improve the NegEx performance resulting in precision of 98.9% and recall of 95.7%. Features related to pancreatic cysts were also extracted from patient medical records using regex and NegEx algorithm with 98.5% precision and 97.43% recall. SDP improved the NegEx algorithm by increasing the recall to 98.12%.

Original languageEnglish (US)
Title of host publicationMEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
PublisherIOS Press
Pages822-826
Number of pages5
Edition1-2
ISBN (Print)9781614992882
DOIs
StatePublished - Jan 1 2013
Event14th World Congress on Medical and Health Informatics, MEDINFO 2013 - Copenhagen, Denmark
Duration: Aug 20 2013Aug 23 2013

Publication series

NameStudies in Health Technology and Informatics
Number1-2
Volume192
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other14th World Congress on Medical and Health Informatics, MEDINFO 2013
CountryDenmark
CityCopenhagen
Period8/20/138/23/13

Keywords

  • dependency parser
  • Natural language processing
  • negation
  • Pancreatic cyst
  • Unstructured Information Management Architecture

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management
  • Medicine(all)

Fingerprint Dive into the research topics of 'An efficient pancreatic cyst identification methodology using natural language processing'. Together they form a unique fingerprint.

Cite this