Journal of Digital Imaging

, Volume 26, Issue 4, pp 709–713 | Cite as

Automatic Retrieval of Bone Fracture Knowledge Using Natural Language Processing

  • Bao H. DoEmail author
  • Andrew S. Wu
  • Joan Maley
  • Sandip Biswal


Natural language processing (NLP) techniques to extract data from unstructured text into formal computer representations are valuable for creating robust, scalable methods to mine data in medical documents and radiology reports. As voice recognition (VR) becomes more prevalent in radiology practice, there is opportunity for implementing NLP in real time for decision-support applications such as context-aware information retrieval. For example, as the radiologist dictates a report, an NLP algorithm can extract concepts from the text and retrieve relevant classification or diagnosis criteria or calculate disease probability. NLP can work in parallel with VR to potentially facilitate evidence-based reporting (for example, automatically retrieving the Bosniak classification when the radiologist describes a kidney cyst). For these reasons, we developed and validated an NLP system which extracts fracture and anatomy concepts from unstructured text and retrieves relevant bone fracture knowledge. We implement our NLP in an HTML5 web application to demonstrate a proof-of-concept feedback NLP system which retrieves bone fracture knowledge in real time.


Natural language processing Decision support Information retrieval of bone fractures 


Acknowledgment of Grants


Financial Disclosures or Other Assistance



  1. 1.
    Do BH, Wu A, Biswal S, Kamaya A, Rubin DL: Informatics in radiology: RADTF: a semantic search-enabled, natural language processor-generated radiology teaching file. Radiographics 30(7):2039–2048, 2010PubMedCrossRefGoogle Scholar
  2. 2.
    Lacson R, Khorasani R: Practical examples of natural language processing in radiology. J Am Coll Radiol 8(12):872–874, 2011PubMedCrossRefGoogle Scholar
  3. 3.
    Hripcsak G, Austin J, Alderson P, Friedman C: Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology 224(1):157–163, 2002PubMedCrossRefGoogle Scholar
  4. 4.
    Sistrom CL, Dreyer KJ, Dang PA, Weilburg JB, Boland GW, Rosenthal DI, Thrall J: Recommendations for additional imaging in radiology reports: multifactorial analysis of 5.9 million examinations. Radiology 53(2):453–461, 2009CrossRefGoogle Scholar
  5. 5.
    Dang PA, Kalra MK, Blake MA, Schultz TJ, Halpern EF, Dreyer KJ: Original research: extraction of recommendation features in radiology with natural language processing: exploratory study. AJR Am J Roentgenol 191(2):313–320, 2008PubMedCrossRefGoogle Scholar
  6. 6.
    Thomas BJ, Ouellette H, Halpern EF, Rosenthal DI: Automated computer-assisted categorization of radiology reports. AJR Am J Roentgenol 184(2):687–690, 2005PubMedCrossRefGoogle Scholar
  7. 7.
    Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T: Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 306(8):848–855, 2011PubMedCrossRefGoogle Scholar
  8. 8.
    Mehta A, Dreyer KJ, Schweitzer A, Couris J, Rosenthal D: Voice recognition—an emerging necessity within radiology: experiences of the Massachusetts General Hospital. J Digit Imaging 11(4 Suppl 2):20–23, 1998PubMedCrossRefGoogle Scholar
  9. 9.
    Quint DJ: Voice recognition: ready for prime time? J Am Coll Radiol 4(10):667–669, 2007PubMedCrossRefGoogle Scholar
  10. 10.
    Mehta A, McLoud TC: Voice recognition. J Thoracic Imaging 18:178–182, 2003CrossRefGoogle Scholar
  11. 11.
    Pezzullo J, Tung GA, Rogg JM, Davis LM, Brody JM, Mayo-Smith WW: Voice recognition dictation: radiologist as transcriptionist. J Digit Imaging 21(4):384–389, 2008PubMedCrossRefGoogle Scholar
  12. 12.
    Wu AS, Do BH, Kim J, Rubin DL: Evaluation of negation and uncertainty detection and its impact on precision and recall in search. J Digit Imaging 24(2):234–242, 2011PubMedCrossRefGoogle Scholar
  13. 13.
    Clifford R. Wheeless III, MD. Wheeless’ Textbook of Orthopaedics.
  14. 14.
    Greenspan A. Orthopedic Imaging—A Practical Approach, fourth edition. Baltimore: Lippincott Williams & Wilkins, 2004.Google Scholar
  15. 15.
    Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG: A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 34(5):301–310, 2001PubMedCrossRefGoogle Scholar
  16. 16.
    Lakhani P, Kim W, Langlotz CP: Automated detection of critical results in radiology reports. J Digit Imaging 25(1):30–36, 2012PubMedCrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2012

Authors and Affiliations

  • Bao H. Do
    • 1
    • 4
    Email author
  • Andrew S. Wu
    • 2
  • Joan Maley
    • 3
  • Sandip Biswal
    • 1
  1. 1.Division of Musculoskeletal Section, Department of RadiologyStanford University School of MedicineStanfordUSA
  2. 2.Department of RadiologyKaiser Permanente Downey Medical CenterDowneyUSA
  3. 3.Division of Neuroradiology, Department of RadiologyUniversity of IowaIowa CityUSA
  4. 4.Division of Musculoskeletal Imaging, Department of RadiologyStanford University Medical CenterStanfordUSA

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