Article

Automated epilepsy and seizure type phenotyping with pre-trained language models

2026-02-22

Abstract excerpt

<h4>Background</h4> Epilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures. Epilepsy manifests as different seizure types and epilepsy types, which have important implications for treatment and prognosis. Electronic health record systems containing longitudinal data on large epilepsy cohorts can be valuable resources for clinical research. However, detailed epilepsy phenotypes ar...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 19c88b34-8ee9-5ace-8e9e-88a2731d3f1a
DOI: 10.64898/2026.02.11.26346003

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Automated epilepsy and seizure type phenotyping with pre-trained language modelsDOI 10.64898/2026.02.11.26346003Read

Evaluating large language models for predicting psychiatric acute readmissions from clinical notes of population-based EHRSemantic proximity Read

Automated Interpretation of EEG Reports Using a Large Language Model with Structured Confidence OutputsSemantic proximity Read

Language Model Applications for Early Diagnosis of Childhood EpilepsySemantic proximity Read

Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysisSemantic proximity Read

Large-Scale Psychiatric Concept Extraction from Electronic Health Records: A Comparative Study of Encoder-Based Language ModelsSemantic proximity Read

Applying natural language processing and large language models to clinical notes for phenotyping and diagnosing rare diseases: a systematic review.Semantic proximity Read

Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanksSemantic proximity Read

A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health recordsSemantic proximity Read

Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notesSemantic proximity Read

Zero-shot extraction of seizure outcomes from clinical notes using generative pretrained transformersSemantic proximity Read

Genetic Transformer: An Innovative Large Language Model Driven Approach for Rapid and Accurate Identification of Causative Variants in Rare Genetic DiseasesSemantic proximity Read

Developing a natural language processing system using transformer-based models for adverse drug event detection in electronic health recordsSemantic proximity Read