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Journal of Digital Imaging

, Volume 25, Issue 1, pp 30–36 | Cite as

Automated Detection of Critical Results in Radiology Reports

  • Paras Lakhani
  • Woojin Kim
  • Curtis P. Langlotz
Article

Abstract

The goal of this study was to develop and validate text-mining algorithms to automatically identify radiology reports containing critical results including tension or increasing/new large pneumothorax, acute pulmonary embolism, acute cholecystitis, acute appendicitis, ectopic pregnancy, scrotal torsion, unexplained free intraperitoneal air, new or increasing intracranial hemorrhage, and malpositioned tubes and lines. The algorithms were developed using rule-based approaches and designed to search for common words and phrases in radiology reports that indicate critical results. Certain text-mining features were utilized such as wildcards, stemming, negation detection, proximity matching, and expanded searches with applicable synonyms. To further improve accuracy, the algorithms utilized modality and exam-specific queries, searched under the “Impression” field of the radiology report, and excluded reports with a low level of diagnostic certainty. Algorithm accuracy was determined using precision, recall, and F-measure using human review as the reference standard. The overall accuracy (F-measure) of the algorithms ranged from 81% to 100%, with a mean precision and recall of 96% and 91%, respectively. These algorithms can be applied to radiology report databases for quality assurance and accreditation, integrated with existing dashboards for display and monitoring, and ported to other institutions for their own use.

Keywords

Algorithms Communication Critical Results Reporting Data Mining Natural Language Processing Quality Assurance Quality Control 

Notes

Acknowledgements

This research was funded by the Society for Imaging Informatics (SIIM) Research Grant.

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Copyright information

© Society for Imaging Informatics in Medicine 2011

Authors and Affiliations

  • Paras Lakhani
    • 1
  • Woojin Kim
    • 1
  • Curtis P. Langlotz
    • 1
  1. 1.Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaUSA

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