Clinician-driven automated classification of limb fractures from free-text radiology reports
Wagholikar, Amol, Zuccon, Guido, Nguyen, Anthony, Chu, Kevin, Martin, Shane, Lai, Kim, & Greenslade, Jaimi (2012) Clinician-driven automated classification of limb fractures from free-text radiology reports. In 2nd Australian Workshop on Artificial Intelligence in Health (AIH 2012), 4 December 2012, Sydney Harbour Marriott Hotel, Sydney, NSW.
The aim of this research is to report initial experimental results and evaluation of a clinician-driven automated method that can address the issue of misdiagnosis from unstructured radiology reports. Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to disperse information resources and vast amounts of manual processing of unstructured information, a point-of-care accurate diagnosis is often difficult. A rule-based method that considers the occurrence of clinician specified keywords related to radiological findings was developed to identify limb abnormalities, such as fractures. A dataset containing 99 narrative reports of radiological findings was sourced from a tertiary hospital. The rule-based method achieved an F-measure of 0.80 and an accuracy of 0.80. While our method achieves promising performance, a number of avenues for improvement were identified using advanced natural language processing (NLP) techniques.
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|Item Type:||Conference Paper|
|Divisions:||Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 [please consult the author]|
|Deposited On:||17 Jun 2014 22:46|
|Last Modified:||20 Jun 2014 00:00|
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