BishtRef
Articles Journals Publishers Authors Subjects Most Cited New Papers Year Stats Open Access
journal article Open Access Dec 19, 2022

Transforming epilepsy research: A systematic review on natural language processing applications

Arister N. J. Yew Marijn Schraagen Willem M. Otte ORCID Eric van Diessen ORCID
Epilepsia Vol. 64 No. 2 pp. 292-305 · Wiley
View at Publisher Save 10.1111/epi.17474
Abstract
AbstractDespite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision‐making. To this end, clinical researchers increasing apply natural language processing (NLP)—a branch of artificial intelligence—as it removes ambiguity, derives context, and imbues standardized meaning from free‐narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a “natural language processing” and “epilepsy” query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty‐six studies were included. Fifty‐eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP‐aided quality evaluation for clinical decision‐making, outcome prediction, and clinical record summarization.
Topics

No keywords indexed for this article. Browse by subject →

References
68
[3]
Epilepsy in adults

Roland D Thijs, Rainer Surges, Terence J O'Brien et al.

The Lancet 10.1016/s0140-6736(18)32596-0
[5]
Electronic health records to facilitate clinical research

Martin R. Cowie, Juuso I. Blomster, Lesley H. Curtis et al.

Clinical Research in Cardiology 10.1007/s00392-016-1025-6
[8]
Savova G (2016)
[14]
A Survey on Deep Learning

Samira Pouyanfar, Saad Sadiq, Yilin Yan et al.

ACM Computing Surveys 10.1145/3234150
[15]
Page MJ "Statement: an updated guideline for reporting systematic reviews" Int J Surg (2020)
[18]
Rijo R "Decision support system to diagnosis and classification of epilepsy in children" J Univ Comput Sci (2014)
[19]
Sullivan R "Text classification towards detecting misdiagnosis of an epilepsy syndrome in a pediatric population" AMIA Annu Symp Proc (2014)
[20]
Biswal S "Automated information extraction from free‐text EEG reports" Annu Int Conf IEEE Eng Med Biol Soc (2015)
[32]
Cui L "EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification" AMIA Annu Symp Proc (2012)
[33]
Zhang GQ "MEDCIS: multi‐modality epilepsy data capture and integration system" AMIA Annu Symp Proc (2014)
[43]
Banerji A "Natural language processing combined with ICD‐9‐CM codes as a novel method to study the epidemiology of allergic drug reactions" J Allergy Clin Immunol (2020)
[44]
Stephens KA "Leveraging UMLS‐driven NLP to enhance identification of influenza predictors derived from electronic medical record data" bioRxiv (2020)

Showing 50 of 68 references

Cited By
49
Artificial intelligence in epilepsy phenotyping

Andrew Knight, Tilo Gschwind · 2024

Epilepsia
Long‐term epilepsy outcome dynamics revealed by natural language processing of clinic notes

Kevin Xie, Ryan S. Gallagher · 2023

Epilepsia
Using artificial intelligence to improve public health: a narrative review

David B. Olawade, Ojima J. Wada · 2023

Frontiers in Public Health
Metrics
49
Citations
68
References
Details
Published
Dec 19, 2022
Vol/Issue
64(2)
Pages
292-305
License
View
Authors
A
Arister N. J. Yew

University College Utrecht, Utrecht University Utrecht The Netherlands

M
Marijn Schraagen

Department of Information and Computing Sciences, Faculty of Science Utrecht University Utrecht The Netherlands

W
Willem M. Otte

Department of Child Neurology, Brain Center University Medical Center Utrecht and Utrecht University Utrecht The Netherlands

E
Eric van Diessen

Department of Child Neurology, Brain Center University Medical Center Utrecht and Utrecht University Utrecht The Netherlands

Cite This Article
Arister N. J. Yew, Marijn Schraagen, Willem M. Otte, et al. (2022). Transforming epilepsy research: A systematic review on natural language processing applications. Epilepsia, 64(2), 292-305. https://doi.org/10.1111/epi.17474
Related

You May Also Like

ILAE Official Report: A practical clinical definition of epilepsy

Robert S. Fisher, Carlos Acevedo · 2014

4,870 citations

ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classification and Terminology

Ingrid E. Scheffer, Samuel Berkovic · 2017

4,486 citations

Definition of drug resistant epilepsy: Consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies

Patrick Kwan, Alexis Arzimanoglou · 2010

3,977 citations

Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology

Robert S. Fisher, J. Helen Cross · 2017

2,934 citations

Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE)

Robert S. Fisher, Walter van Emde Boas · 2005

2,913 citations