ArticleSeizure2022
Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.
Article in Seizure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- [Structured Annotation and Information Extraction of Epilepsy Clinical Texts].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Using clinical notes to identify children with speech-language delay and understand differences in diagnostic timing.JAMIA open · 2026Article
- Evaluating large language models for diagnostic reasoning from unstructured clinical narratives in epilepsy.Communications medicine · 2026Article
- Extracting epilepsy-related information from unstructured clinic letters using large language models.Epilepsia · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Leveraging pretrained language models for seizure frequency extraction from epilepsy evaluation reports.NPJ digital medicine · 2025Article
- Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.Frontiers in neurology · 2025Article
- Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.Epilepsy research · 2024Article
- Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.JMIR medical informatics · 2024Article
- Annotation of epilepsy clinic letters for natural language processing.Journal of biomedical semantics · 2024Article
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
- Article
- Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.Epilepsia · 2023Article
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Authors and funding
11 authors.
Funding
Abstract
objectiveTo develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).
backgroundSeizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text of clinic notes. Algorithm performance was assessed at two epilepsy centers.
methodsWe developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type within a time interval) was compared to seizure data manually annotated by two expert reviewers ("gold standard"). The algorithm was developed from 150 clinic notes from institution #1 (development set), then tested on a separate set of 219 notes from institution #1 (internal test set) with 248 unique seizure frequency elements. The algorithm was separately applied to 100 notes from institution #2 (external test set) with 124 unique seizure frequency elements. Algorithm performance was measured by recall (sensitivity), precision (positive predictive value), and F1 score (geometric mean of precision and recall).
resultsIn the internal test set, the algorithm demonstrated 70% recall (173/248), 95% precision (173/182), and 0.82 F1 score compared to manual review. Algorithm performance in the external test set was lower with 22% recall (27/124), 73% precision (27/37), and 0.40 F1 score.
conclusionsThese results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.