Complex epilepsy phenotype extraction from narrative clinical discharge summaries
Identifieur interne : 000133 ( Pmc/Corpus ); précédent : 000132; suivant : 000134Complex epilepsy phenotype extraction from narrative clinical discharge summaries
Auteurs : Licong Cui ; Satya S. Sahoo ; Samden D. Lhatoo ; Gaurav Garg ; Prashant Rai ; Alireza Bozorgi ; Guo-Qiang ZhangSource :
- 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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