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

Wagholikar, A., Zuccon, G., Nguyen, A., Chu, K., Martin, S., Lai, K. and Greenslade, J. (2013) Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology. Australasian Medical Journal, 6 5: 301-307. doi:10.4066/AMJ.2013.1651

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Author Wagholikar, A.
Zuccon, G.
Nguyen, A.
Chu, K.
Martin, S.
Lai, K.
Greenslade, J.
Title Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology
Journal name Australasian Medical Journal   Check publisher's open access policy
ISSN 1836-1935
Publication date 2013
Sub-type Article (original research)
DOI 10.4066/AMJ.2013.1651
Open Access Status DOI
Volume 6
Issue 5
Start page 301
End page 307
Total pages 7
Place of publication Perth, W.A., Australia
Publisher Australasian Medical Journal Pty. Ltd.
Collection year 2014
Language eng
Subject 2700 Medicine
Formatted 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.

Keyword Classification
Emergency department
Limb fractures
Machine learning
Radiology reports
Rule-based method
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Medicine Publications
 
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Created: Mon, 17 Mar 2014, 08:04:35 EST by Matthew Lamb on behalf of School of Medicine