Literature DB >> 32472318

Framework for Extracting Critical Findings in Radiology Reports.

Thusitha Mabotuwana1,2, Christopher S Hall3,4, Nathan Cross4.   

Abstract

Critical results reporting guidelines demand that certain critical findings are communicated to the responsible provider within a specific period of time. In this paper, we discuss a generic report processing pipeline to extract critical findings within the dictated report to allow for automation of quality and compliance oversight using a production dataset containing 1,210,858 radiology exams. Algorithm accuracy on an annotated dataset having 327 sentences was 91.4% (95% CI 87.6-94.2%). Our results show that most critical findings are diagnosed on CT and MR exams and that intracranial hemorrhage and fluid collection are the most prevalent at our institution. 1.6% of the exams were found to have at least one of the ten critical findings we focused on. This methodology can enable detailed analysis of critical results reporting for research, workflow management, compliance, and quality assurance.

Entities:  

Keywords:  Critical results; Radiology informatics; Radiology reports; Rule-based systems

Year:  2020        PMID: 32472318      PMCID: PMC7522156          DOI: 10.1007/s10278-020-00349-7

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  7 in total

1.  Automated detection of critical results in radiology reports.

Authors:  Paras Lakhani; Woojin Kim; Curtis P Langlotz
Journal:  J Digit Imaging       Date:  2012-02       Impact factor: 4.056

2.  Optimizing communication of critical test results.

Authors:  Ramin Khorasani
Journal:  J Am Coll Radiol       Date:  2009-10       Impact factor: 5.532

3.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Authors:  Paras Lakhani; Baskaran Sundaram
Journal:  Radiology       Date:  2017-04-24       Impact factor: 11.105

4.  Actionable findings and the role of IT support: report of the ACR Actionable Reporting Work Group.

Authors:  Paul A Larson; Lincoln L Berland; Brent Griffith; Charles E Kahn; Lawrence A Liebscher
Journal:  J Am Coll Radiol       Date:  2014-01-30       Impact factor: 5.532

5.  Failure to recognize newly identified aortic dilations in a health care system with an advanced electronic medical record.

Authors:  Jennifer R S Gordon; Terry Wahls; Ruth C Carlos; Iraklis I Pipinos; Gary E Rosenthal; Peter Cram
Journal:  Ann Intern Med       Date:  2009-07-07       Impact factor: 25.391

6.  Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

Authors:  P Chang; J Grinband; B D Weinberg; M Bardis; M Khy; G Cadena; M-Y Su; S Cha; C G Filippi; D Bota; P Baldi; L M Poisson; R Jain; D Chow
Journal:  AJNR Am J Neuroradiol       Date:  2018-05-10       Impact factor: 3.825

7.  Retrieval of radiology reports citing critical findings with disease-specific customization.

Authors:  Ronilda Lacson; Nathanael Sugarbaker; Luciano M Prevedello; Ip Ivan; Wendy Mar; Katherine P Andriole; Ramin Khorasani
Journal:  Open Med Inform J       Date:  2012-08-10
  7 in total

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