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J Biomed Inform. 2014 Oct;51:272-9. doi: 10.1016/j.jbi.2014.06.006. Epub 2014 Jun 26.

Complex epilepsy phenotype extraction from narrative clinical discharge summaries.

Author information

1
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.
2
Division of Medical Informatics, Case Western Reserve University, Cleveland, OH 44106, USA.
3
Department of Neurology, Case Western Reserve University, Cleveland, OH 44106, USA.
4
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA; Division of Medical Informatics, Case Western Reserve University, Cleveland, OH 44106, USA. Electronic address: gq@case.edu.

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 F1-measure of 0.927 for extracting epilepsy phenotypes. The performance on the extraction of correlated phenotypes and anatomical locations shows a micro-averaged F1-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.

KEYWORDS:

Cohort identification; Epilepsy; Information extraction

PMID:
24973735
PMCID:
PMC4464795
DOI:
10.1016/j.jbi.2014.06.006
[Indexed for MEDLINE]
Free PMC Article

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