Complex epilepsy phenotype extraction from narrative clinical discharge summaries

Licong Cui, Satya S. Sahoo, Samden D. Lhatoo, Gaurav Garg, Prashant Rai, Alireza Bozorgi, Guo Qiang Zhang

Research output: Contribution to journalArticle

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)272-279
Number of pages8
JournalJournal of Biomedical Informatics
Volume51
DOIs
StatePublished - Oct 1 2014
Externally publishedYes

Keywords

  • Cohort identification
  • Epilepsy
  • Information extraction

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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