Literature DB >> 10772780

Coding neuroradiology reports for the Northern Manhattan Stroke Study: a comparison of natural language processing and manual review.

J S Elkins1, C Friedman, B Boden-Albala, R L Sacco, G Hripcsak.   

Abstract

Automated systems using natural language processing may greatly speed chart review tasks for clinical research, but their accuracy in this setting is unknown. The objective of this study was to compare the accuracy of automated and manual coding in the data acquisition tasks of an ongoing clinical research study, the Northern Manhattan Stroke Study(NOMASS). We identified 471 neuroradiology reports of brain images used in the NOMASS study. Using both automated and manual coding, we completed a standardized NOMASS imaging form with the information contained in these reports. We then generated ROC curves for both manual and automated coding by comparing our results to the original NOMASS data, where study in investigators directly coded their interpretations of brain images. The areas under the ROC curves for both manual and automated coding were the main outcome measure. The overall predictive value of the automated system (ROC area 0.85, 95% CI 0.84-0.87) was not statistically different from the predictive value of the manual coding (ROC area 0.87, 95% CI 0.83-0.91). Measured in terms of accuracy, the automated system performed slightly worse than manual coding. The overall accuracy of the automated system was 84% (CI 83-85%). The overall accuracy of manual coding was 86% (CI 84-88%). The difference in accuracy between the two methods was small but statistically significant (P = 0.026). Errors in manual coding appeared to be due to differences between neurologists' and nueroradiologists' interpretation, different use of detailed anatomic terms, and lack of clinical information. Automated systems can use natural language processing to rapidly perform complex data acquisition tasks. Although there is a small decrease in the accuracy of the data as compared to traditional methods, automated systems may greatly expand the power of chart review in clinical research design and implementation. Copyright 2000 Academic Press.

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Year:  2000        PMID: 10772780     DOI: 10.1006/cbmr.1999.1535

Source DB:  PubMed          Journal:  Comput Biomed Res        ISSN: 0010-4809


  22 in total

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Authors:  Wendy Webber Chapman; Gregory F Cooper; Paul Hanbury; Brian E Chapman; Lee H Harrison; Michael M Wagner
Journal:  J Am Med Inform Assoc       Date:  2003-06-04       Impact factor: 4.497

3.  Automated encoding of clinical documents based on natural language processing.

Authors:  Carol Friedman; Lyudmila Shagina; Yves Lussier; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2004-06-07       Impact factor: 4.497

Review 4.  A systematic literature review of automated clinical coding and classification systems.

Authors:  Mary H Stanfill; Margaret Williams; Susan H Fenton; Robert A Jenders; William R Hersh
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5.  Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository.

Authors:  Saeed Hassanpour; Curtis P Langlotz
Journal:  J Digit Imaging       Date:  2016-02       Impact factor: 4.056

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Authors:  Stephane Meystre; Peter J Haug
Journal:  BMC Med Inform Decis Mak       Date:  2005-08-31       Impact factor: 2.796

7.  MediClass: A system for detecting and classifying encounter-based clinical events in any electronic medical record.

Authors:  Brian Hazlehurst; H Robert Frost; Dean F Sittig; Victor J Stevens
Journal:  J Am Med Inform Assoc       Date:  2005-05-19       Impact factor: 4.497

8.  Generating models of surgical procedures using UMLS concepts and multiple sequence alignment.

Authors:  Frank Meng; Leonard W D'Avolio; Andrew A Chen; Ricky K Taira; Hooshang Kangarloo
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9.  Use of Radcube for extraction of finding trends in a large radiology practice.

Authors:  Pragya A Dang; Mannudeep K Kalra; Michael A Blake; Thomas J Schultz; Markus Stout; Elkan F Halpern; Keith J Dreyer
Journal:  J Digit Imaging       Date:  2008-06-10       Impact factor: 4.056

10.  A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Authors:  Curtis P Langlotz; Bibb Allen; Bradley J Erickson; Jayashree Kalpathy-Cramer; Keith Bigelow; Tessa S Cook; Adam E Flanders; Matthew P Lungren; David S Mendelson; Jeffrey D Rudie; Ge Wang; Krishna Kandarpa
Journal:  Radiology       Date:  2019-04-16       Impact factor: 11.105

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