Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.

ABSTRACT:

Introduction

Electroencephalography (EEG) is a popular technique that provides neurologists with electrographic insights and clinical interpretations. However, these insights are predominantly presented in unstructured textual formats, which complicates data extraction and analysis. In this study, we introduce a hierarchical algorithm aimed at transforming unstructured EEG reports from pediatric patients diagnosed with epilepsy into structured data using natural language processing (NLP) techniques.

Methods

The proposed algorithm consists of two distinct phases: a deep learning-based text classification followed by a series of rule-based keyword extraction procedures. First, we categorized the EEG reports into two primary groups: normal and abnormal. Thereafter, we systemati

SUBMITTER: Chung YG 

PROVIDER: S-EPMC11906308 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature