Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Complex epilepsy phenotype extraction from narrative clinical discharge summaries.

Journal of biomedical informatics - 1 Oct 2014

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

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Artificial Intelligence
Share to Biological Ontologies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Data Mining

Share to Electroencephalography

Share to Epilepsy

Share to Natural Language Processing

Share to Pattern Recognition, Automated

Share to Semantics

Source

Open in PubMed Open full text

Identifiers

PMID: 24973735
DOI: 10.1016/j.jbi.2014.06.006
PMCID: PMC4464795

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026. Manifest publication-graph-cohort-2026-09-05.1.

Source receipt: pubmed, retrieved 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Complex epilepsy phenotype extraction from narrative clinical discharge summaries.PMID 24973735Read ↗

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.Semantic proximity Read ↗

Improving Patient Similarity Using Different Modalities of Phenotypes Extracted from Clinical Narratives.Semantic proximity Read ↗

Using Human Phenotype Ontology for Phenotypic Analysis of Clinical Notes.Semantic proximity Read ↗

Ensembles of natural language processing systems for portable phenotyping solutions.Semantic proximity Read ↗

Next generation phenotyping using narrative reports in a rare disease clinical data warehouse.Semantic proximity Read ↗

Construction of cohorts of similar patients from automatic extraction of medical conceptsSemantic proximity Read ↗

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.Semantic proximity Read ↗

Phenotypic Analysis of Clinical Narratives Using Human Phenotype Ontology.Semantic proximity Read ↗

Automated epilepsy and seizure type phenotyping with pre-trained language modelsSemantic proximity Read ↗

Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning ApproachSemantic proximity Read ↗

Towards automated phenotype definition extraction using large language modelsSemantic proximity Read ↗

Extracting phenotypic information from the literature via natural language processing.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.