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Epilepsy and Seizures: Overview

Epilepsy and Seizures: Overview

Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...

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Updated: Mar 20, 2026

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

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Published on: September 20, 2024

Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and

Kevin Bretonnel Cohen¹, Benjamin Glass², Hansel M Greiner³

¹Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.

Biomedical Informatics Insights

|June 4, 2016

Summary

Machine learning and natural language processing can identify pediatric epilepsy surgery candidates as effectively and earlier than physicians. This system uses electronic health records to predict surgical intervention needs, potentially reducing patient wait times.

Keywords:

epilepsy epilepsy surgery machine learning natural language processing neurosurgery

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Published on: December 18, 2016

Area of Science:

Medical Informatics
Computational Neuroscience
Artificial Intelligence in Medicine

Background:

Drug-resistant pediatric epilepsy requires timely surgical intervention.
Identifying suitable candidates often involves complex clinical data analysis.
Current methods may lead to delays in surgical referral.

Purpose of the Study:

To develop and evaluate a machine learning (ML) and natural language processing (NLP) system for identifying pediatric epilepsy surgery candidates.
To test if ML methods can match physician performance in identifying candidates.
To determine if ML methods can identify candidates earlier than physicians.

Main Methods:

Utilized free-text clinical notes from electronic health records (EHR).
Employed ML and NLP techniques for candidate identification.
Systematically evaluated data source, training data volume, class balance, algorithm, and feature set impact on performance.

Main Results:

ML methods demonstrated comparable performance to physicians in identifying epilepsy surgery candidates (F-measures 0.71-0.82).
ML identified candidates earlier than physicians, outperforming inter-annotator agreement even a year before referral.
Feature set, algorithm, training data, class balance, and gold standard significantly influenced performance.

Conclusions:

ML and NLP systems can effectively identify pediatric epilepsy surgery candidates from EHR data.
These methods show potential to reduce the time lag to surgical referral.
The developed system offers a promising tool for optimizing epilepsy treatment pathways.