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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

Stage–wise experiments. A total of ten concepts were incrementally removed from the ontology
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Fig14: Stage–wise experiments. A total of ten concepts were incrementally removed from the ontology

Mentions: Having identified just over 100 of such concepts, we randomly selected 100 of them, randomized their order and removed top k of these concepts (k = 10, 20, … , 100) from the ontology, which was then used to run KneeTex on the gold standard. Figure 14, provides a comparison of evaluation results. As expected, completeness of the ontology directly affected the recall of the system. This was most obvious when frequently referenced concepts such as body of meniscus (TRAK:0001346) or joint effusion (TRAK:0001411) were removed. However, the frequency and meaning of these concepts imply that they are of general relevance to the domain and not the result of overfitting to the training dataset. On the other side, the removal of less frequently referenced concepts did not have a profound effect on recall. For example, after removing as many as 50 concepts from the ontology, recall was still very high at 92.07 % dropping by 5.57 percent points. Precision proved to be more stable reaching 94.48 % after removing all 100 concepts dropping only by 3.52 percent points.Fig. 14


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

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

Stage–wise experiments. A total of ten concepts were incrementally removed from the ontology
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig14: Stage–wise experiments. A total of ten concepts were incrementally removed from the ontology
Mentions: Having identified just over 100 of such concepts, we randomly selected 100 of them, randomized their order and removed top k of these concepts (k = 10, 20, … , 100) from the ontology, which was then used to run KneeTex on the gold standard. Figure 14, provides a comparison of evaluation results. As expected, completeness of the ontology directly affected the recall of the system. This was most obvious when frequently referenced concepts such as body of meniscus (TRAK:0001346) or joint effusion (TRAK:0001411) were removed. However, the frequency and meaning of these concepts imply that they are of general relevance to the domain and not the result of overfitting to the training dataset. On the other side, the removal of less frequently referenced concepts did not have a profound effect on recall. For example, after removing as many as 50 concepts from the ontology, recall was still very high at 92.07 % dropping by 5.57 percent points. Precision proved to be more stable reaching 94.48 % after removing all 100 concepts dropping only by 3.52 percent points.Fig. 14

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