KneeTex: an ontology-driven system for information extraction from MRI reports

Journal of Biomedical Semantics
Irena SpasićKate Button

Abstract

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-seman...Continue Reading

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Citations

Sep 13, 2017·Journal of Biomedical Semantics·Louise DelégerAurélie Névéol
Feb 6, 2020·BMC Musculoskeletal Disorders·Kate ButtonStephen Jones
Aug 1, 2021·BMC Medical Informatics and Decision Making·Xin HuangJing-Dong Yan

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Methods Mentioned

BETA
X-ray
X–ray

Software Mentioned

MetaMap
FlexiTerm
PathNER
MEDCIN
Apache UIMA Sandbox
BioPortal
Mixup ( My Information eXtraction and Understanding Package )
MongoDB
OntoCAT
Medicomp

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