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

Spasić I, Zhao B, Jones CB, Button K - J Biomed Semantics (2015)

Bottom Line: Therefore, clinical narratives found in MRI reports convey valuable diagnostic information.Information extraction results were evaluated on a test set of 100 MRI reports.As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science & Informatics, Cardiff University, Cardiff, CF24 3AA UK.

ABSTRACT

Background: 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.

Methods: 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-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process.

Results: We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00 %, recall of 97.63 % and F-measure of 97.81 %, the values of which are in line with human-like performance.

Conclusions: KneeTex is an open-source, stand-alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

No MeSH data available.


Related in: MedlinePlus

MEDCIN terminology related to MRI of knee. UMLS terminology services were used to access relevant terminology
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Fig6: MEDCIN terminology related to MRI of knee. UMLS terminology services were used to access relevant terminology

Mentions: The first source, MEDCIN, was identified through the UMLS terminology services [44]. MEDCIN is a medical terminology created and maintained by Medicomp Systems, Inc. as part of their system for management of clinical information [45, 46]. MEDCIN contains more than 250,000 concepts encompassing symptoms, history, physical examination, tests, diagnoses and therapies structured into multiple clinical hierarchies. One such hierarchy with the root element named magnetic resonance imaging of knee represents a detailed taxonomy of findings that can be identified from knee MRI scans. We extracted this particular taxonomy from the UMLS by using MRI knee as a search term restricted to MEDCIN as the source vocabulary (Fig. 6 illustrates the search results).Fig. 6


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

Spasić I, Zhao B, Jones CB, Button K - J Biomed Semantics (2015)

MEDCIN terminology related to MRI of knee. UMLS terminology services were used to access relevant terminology
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4561435&req=5

Fig6: MEDCIN terminology related to MRI of knee. UMLS terminology services were used to access relevant terminology
Mentions: The first source, MEDCIN, was identified through the UMLS terminology services [44]. MEDCIN is a medical terminology created and maintained by Medicomp Systems, Inc. as part of their system for management of clinical information [45, 46]. MEDCIN contains more than 250,000 concepts encompassing symptoms, history, physical examination, tests, diagnoses and therapies structured into multiple clinical hierarchies. One such hierarchy with the root element named magnetic resonance imaging of knee represents a detailed taxonomy of findings that can be identified from knee MRI scans. We extracted this particular taxonomy from the UMLS by using MRI knee as a search term restricted to MEDCIN as the source vocabulary (Fig. 6 illustrates the search results).Fig. 6

Bottom Line: Therefore, clinical narratives found in MRI reports convey valuable diagnostic information.Information extraction results were evaluated on a test set of 100 MRI reports.As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science & Informatics, Cardiff University, Cardiff, CF24 3AA UK.

ABSTRACT

Background: 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.

Methods: 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-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process.

Results: We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00 %, recall of 97.63 % and F-measure of 97.81 %, the values of which are in line with human-like performance.

Conclusions: KneeTex is an open-source, stand-alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

No MeSH data available.


Related in: MedlinePlus