Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies
Our primary focus was to convert EEG reports into structured datasets, diverging from the traditional methods of formulating clinical notes or discharge summaries. We developed a hierarchical and streamlined approach leveraging keyword selections guided by neurologists, which contributed to the exceptional performance of our algorithm. Overall, this methodology enhances data accessibility as well as improves the potential for further research and clinical applications in the field of pediatric epilepsy management.
Authors
- Hunmin Kim (ORCID: https://orcid.org/0000-0001-6689-3495)
- Yong Seo Koo (ORCID: https://orcid.org/0000-0001-5418-2522)
- Yoon Gi Chung
- Young‐Min Shon (ORCID: https://orcid.org/0000-0001-5171-1695)
- Jaeso Cho (ORCID: https://orcid.org/0000-0002-2479-3856)
- Hyun Woo Kim (ORCID: https://orcid.org/0000-0003-2473-8360)
- Young Ho Kim (ORCID: https://orcid.org/0000-0002-5281-1185)
- Seo‐Young Lee (ORCID: https://orcid.org/0000-0001-5319-1777)
Institutions
- Ulsan College (KR)
- Seoul National University (KR)
- Kangwon National University (KR)
- Asan Medical Center (KR)
- Samsung Medical Center (KR)
- Seoul National University Bundang Hospital (KR)
- University of Ulsan (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-02-28
- DOI
- https://doi.org/10.3389/fneur.2025.1521001
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48