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Wagholikar A, Zuccon G, Nguyen A, et al. Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology. Australas Med J. 2013;6(5):301-7doi: 10.4066/AMJ.2013.1651.
Wagholikar, A., Zuccon, G., Nguyen, A., Chu, K., Martin, S., Lai, K., & Greenslade, J. (2013). Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology. The Australasian medical journal, 6(5), 301-7. https://doi.org/10.4066/AMJ.2013.1651
Wagholikar, Amol, et al. "Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology." The Australasian medical journal vol. 6,5 (2013): 301-7. doi: https://doi.org/10.4066/AMJ.2013.1651
Wagholikar A, Zuccon G, Nguyen A, Chu K, Martin S, Lai K, Greenslade J. Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology. Australas Med J. 2013 May 30;6(5):301-7. doi: 10.4066/AMJ.2013.1651. Print 2013. PMID: 23745152; PMCID: PMC3674422.
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Australas Med J. 2013 May 30;6(5):301-7. doi: 10.4066/AMJ.2013.1651. Print 2013.

Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology.

The Australasian medical journal

Amol Wagholikar, Guido Zuccon, Anthony Nguyen, Kevin Chu, Shane Martin, Kim Lai, Jaimi Greenslade

Affiliations

  1. The Australian e-Health Research Centre, Brisbane, CSIRO.

PMID: 23745152 PMCID: PMC3674422 DOI: 10.4066/AMJ.2013.1651

Abstract

BACKGROUND: Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to dispersed information resources and a vast amount of manual processing of unstructured information, accurate point-of-care diagnosis is often difficult.

AIMS: The aim of this research is to report initial experimental evaluation of a clinician-informed automated method for the issue of initial misdiagnoses associated with delayed receipt of unstructured radiology reports.

METHOD: A method was developed that resembles clinical reasoning for identifying limb abnormalities. The method consists of a gazetteer of keywords related to radiological findings; the method classifies an X-ray report as abnormal if it contains evidence contained in the gazetteer. A set of 99 narrative reports of radiological findings was sourced from a tertiary hospital. Reports were manually assessed by two clinicians and discrepancies were validated by a third expert ED clinician; the final manual classification generated by the expert ED clinician was used as ground truth to empirically evaluate the approach.

RESULTS: The automated method that attempts to individuate limb abnormalities by searching for keywords expressed by clinicians achieved an F-measure of 0.80 and an accuracy of 0.80.

CONCLUSION: While the automated clinician-driven method achieved promising performances, a number of avenues for improvement were identified using advanced natural language processing (NLP) and machine learning techniques.

Keywords: Limb fractures; classification; emergency department; machine learning; radiology reports; rule-based method

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