期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 51, 期 -, 页码 272-279出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2014.06.006
关键词
Epilepsy; Information extraction; Cohort identification
资金
- Prevention and Risk Identification of SUDEP Mortality Project [1-P20-NS076965-01]
- Case Western Reserve University Clinical and Translational Science Award (CTSC) [UL1TR000439]
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 F-1-measure of 0.927 for extracting epilepsy phenotypes. The performance on the extraction of correlated phenotypes and anatomical locations shows a micro-averaged F-1-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. (C) 2014 Published by Elsevier Inc.
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