ArticleJournal of biomedical informatics2014
Complex epilepsy phenotype extraction from narrative clinical discharge summaries.
Article in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Identification of patients with epilepsy using automated electronic health records phenotyping.Epilepsia · 2023Trial
- AI language model applications for early diagnosis of childhood epilepsy based on unstructured first-visit patient narratives: A cohort study.Epileptic disorders : international epilepsy journal with videotape · 2025Article
- Comparison of rule- and large language model-based phenotype extraction from clinical notes for neurofibromatosis type 1.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Automated extraction of functional biomarkers of verbal and ambulatory ability from multi-institutional clinical notes using large language models.Journal of neurodevelopmental disorders · 2025Article
- Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.Frontiers in neurology · 2025Article
- Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application.BMC medical informatics and decision making · 2024Article
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
- Clinical concept and relation extraction using prompt-based machine reading comprehension.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A replicable, open-source, data integration method to support national practice-based research & quality improvement systems.Epilepsy research · 2022Article
- A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy.Frontiers in big data · 2022Article
- Can Big Data guide prognosis and clinical decisions in epilepsy?Epilepsia · 2021Review
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- BioHackathon 2015: Semantics of data for life sciences and reproducible research.F1000Research · 2020Article
- Big data in status epilepticus.Epilepsy & behavior : E&B · 2019Review
- Active deep learning for the identification of concepts and relations in electroencephalography reports.Journal of biomedical informatics · 2019Article
- ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis.Genetics in medicine : official journal of the American College of Medical Genetics · 2019Article
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Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.