ArticleGenetics in medicine : official journal of the American College of Medical Genetics2020
A longitudinal footprint of genetic epilepsies using automated electronic medical record interpretation.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Modeling Epilepsies with Human Brain Organoids: Recent Advances and Ongoing Challenges.Epilepsy currents · 2026Review
- Epilepsy disease classification: a community effort to enhance the Mondo Disease Ontology.Database : the journal of biological databases and curation · 2026Article
- Quantitative EEG Biomarkers in the Genetic Epilepsies and Associations With Neurologic Outcomes.Neurology · 2025Article
- Variants in BSN, encoding the presynaptic protein Bassoon, result in a distinct neurodevelopmental disorder with a broad phenotypic range.American journal of human genetics · 2025Article
- Deciphering the Natural History ofNeurology · 2025Article
- A Longitudinal Exploration ofNeurology. Genetics · 2025Review
- Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals.Genetics in medicine : official journal of the American College of Medical Genetics · 2024Article
- Healthcare utilization and clinical characteristics of genetic epilepsy in electronic health records.Brain communications · 2024Article
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Enriching representation learning using 53 million patient notes through human phenotype ontology embedding.Artificial intelligence in medicine · 2023Article
- Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.JAMIA open · 2023Article
- Clinical variants inGenetics in medicine open · 2023Article
- Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.EBioMedicine · 2023Article
- Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery.Human mutation · 2022Review
- Precision medicine via the integration of phenotype-genotype information in neonatal genome project.Fundamental research · 2022Review
- PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.BMC medical informatics and decision making · 2022Article
- Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs.medRxiv : the preprint server for health sciences · 2022Article
- Assessing the landscape of STXBP1-related disorders in 534 individuals.Brain : a journal of neurology · 2022Article
- 'Channeling' therapeutic discovery for epileptic encephalopathy through iPSC technologies.Trends in pharmacological sciences · 2022Review
- Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data.European journal of human genetics : EJHG · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
purposeChildhood epilepsies have a strong genetic contribution, but the disease trajectory for many genetic etiologies remains unknown. Electronic medical record (EMR) data potentially allow for the analysis of longitudinal clinical information but this has not yet been explored.
methodsWe analyzed provider-entered neurological diagnoses made at 62,104 patient encounters from 658 individuals with known or presumed genetic epilepsies. To harmonize clinical terminology, we mapped clinical descriptors to Human Phenotype Ontology (HPO) terms and inferred higher-level phenotypic concepts. We then binned the resulting 286,085 HPO terms to 100 3-month time intervals and assessed gene-phenotype associations at each interval.
resultsWe analyzed a median follow-up of 6.9 years per patient and a cumulative 3251 patient years. Correcting for multiple testing, we identified significant associations between "Status epilepticus" with SCN1A at 1.0 years, "Severe intellectual disability" with PURA at 9.75 years, and "Infantile spasms" and "Epileptic spasms" with STXBP1 at 0.5 years. The identified associations reflect known clinical features of these conditions, and manual chart review excluded provider bias.
conclusionSome aspects of the longitudinal disease histories can be reconstructed through EMR data and reveal significant gene-phenotype associations, even within closely related conditions. Gene-specific EMR footprints may enable outcome studies and clinical decision support.
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