Complex epilepsy phenotype extraction from narrative clinical discharge summaries
Identifieur interne : 000133 ( Pmc/Curation ); précédent : 000132; suivant : 000134Complex epilepsy phenotype extraction from narrative clinical discharge summaries
Auteurs : Licong Cui [États-Unis] ; Satya S. Sahoo [États-Unis] ; Samden D. Lhatoo [États-Unis] ; Gaurav Garg [États-Unis] ; Prashant Rai [États-Unis] ; Alireza Bozorgi [États-Unis] ; Guo-Qiang Zhang [États-Unis]Source :
- Journal of biomedical informatics [ 1532-0464 ] ; 2014.
Abstract
Epilepsy is a common serious neurological disorder with a complex set of possible phenotypes ranging from pathologic abnormalities to variations in electroencephalogram. This paper presents a system called Phenotype Exaction in Epilepsy (PEEP) for extracting complex epilepsy phenotypes and their correlated anatomical locations from clinical discharge summaries, a primary data source for this purpose. PEEP generates candidate phenotype and anatomical location pairs by embedding a named entity recognition method, based on the Epilepsy and Seizure Ontology, into the National Library of Medicine's MetaMap program. Such candidate pairs are further processed using a correlation algorithm. The derived phenotypes and correlated locations have been used for cohort identification with an integrated ontology-driven visual query interface. To evaluate the performance of PEEP, 400 de-identified discharge summaries were used for development and an additional 262 were used as test data. PEEP achieved a micro-averaged precision of 0.924, recall of 0.931, and
Url:
DOI: 10.1016/j.jbi.2014.06.006
PubMed: 24973735
PubMed Central: 4464795
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<front><div type="abstract" xml:lang="en"><p id="P1">Epilepsy is a common serious neurological disorder with a complex set of possible phenotypes ranging from pathologic abnormalities to variations in electroencephalogram. This paper presents a system called Phenotype Exaction in Epilepsy (PEEP) for extracting complex epilepsy phenotypes and their correlated anatomical locations from clinical discharge summaries, a primary data source for this purpose. PEEP generates candidate phenotype and anatomical location pairs by embedding a named entity recognition method, based on the Epilepsy and Seizure Ontology, into the National Library of Medicine's MetaMap program. Such candidate pairs are further processed using a correlation algorithm. The derived phenotypes and correlated locations have been used for cohort identification with an integrated ontology-driven visual query interface. To evaluate the performance of PEEP, 400 de-identified discharge summaries were used for development and an additional 262 were used as test data. PEEP achieved a micro-averaged precision of 0.924, recall of 0.931, and <italic>F</italic>
<sub>1</sub>
-measure of 0.927 for extracting epilepsy phenotypes. The performance on the extraction of correlated phenotypes and anatomical locations shows a micro-averaged <italic>F</italic>
<sub>1</sub>
-measure of 0.856 (Precision: 0.852, Recall: 0.859). The evaluation demonstrates that PEEP is an effective approach to extracting complex epilepsy phenotypes for cohort identification.</p>
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Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA</aff>
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Division of Medical Informatics, Case Western Reserve University, Cleveland, OH 44106, USA</aff>
<aff id="A3"><label>c</label>
Department of Neurology, Case Western Reserve University, Cleveland, OH 44106, USA</aff>
<author-notes><corresp id="FN1"><label>*</label>
Corresponding author. Address: Division of Medical Informatics, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH 44106, USA. <email>gq@case.edu</email>
(GQ Zhang)</corresp>
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<abstract><p id="P1">Epilepsy is a common serious neurological disorder with a complex set of possible phenotypes ranging from pathologic abnormalities to variations in electroencephalogram. This paper presents a system called Phenotype Exaction in Epilepsy (PEEP) for extracting complex epilepsy phenotypes and their correlated anatomical locations from clinical discharge summaries, a primary data source for this purpose. PEEP generates candidate phenotype and anatomical location pairs by embedding a named entity recognition method, based on the Epilepsy and Seizure Ontology, into the National Library of Medicine's MetaMap program. Such candidate pairs are further processed using a correlation algorithm. The derived phenotypes and correlated locations have been used for cohort identification with an integrated ontology-driven visual query interface. To evaluate the performance of PEEP, 400 de-identified discharge summaries were used for development and an additional 262 were used as test data. PEEP achieved a micro-averaged precision of 0.924, recall of 0.931, and <italic>F</italic>
<sub>1</sub>
-measure of 0.927 for extracting epilepsy phenotypes. The performance on the extraction of correlated phenotypes and anatomical locations shows a micro-averaged <italic>F</italic>
<sub>1</sub>
-measure of 0.856 (Precision: 0.852, Recall: 0.859). The evaluation demonstrates that PEEP is an effective approach to extracting complex epilepsy phenotypes for cohort identification.</p>
</abstract>
<kwd-group><kwd>Epilepsy</kwd>
<kwd>Information Extraction</kwd>
<kwd>Cohort Identification</kwd>
</kwd-group>
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