Complex epilepsy phenotype extraction from narrative clinical discharge summaries

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.

Figure 1

Epileptogenic Zone, Seizure Semiology, Lateralizing Sign captured in an orignal discharge summary.

Figure 2

Interictal and Ictal EEG findings captured in a source discharge summary.

Figure 3

Pipeline of the Phenotype Exaction in Epilepsy (PEEP) system.

Figure 4

Baseline MetaMapRENER to generate phenotype and anatomical location candidate pairs.

Figure 5

Correlation algorithm to identify pairs of phenotypes and anatomical locations for a document.