{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Le Gac F"],"funding":["Agence Nationale de la Recherche","Direction Générale de l'offre de Soins"],"pagination":["741-752"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12927673"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["67(2)"],"pubmed_abstract":["<h4>Objective</h4>Manual review of clinical notes by experts remains the reference standard for identifying patients with epilepsy in health databases. However, this process is labor-intensive and time-consuming due to the unstructured nature of text. Prior studies have shown the potential of natural language processing for automated phenotyping. We aim to develop and validate algorithms capable of identifying patients with epilepsy based on a set of clinical notes.<h4>Methods</h4>A population of 109 448 patients was selected from the Assistance Publique-Hôpitaux de Paris (AP-HP) Clinical Data Warehouse (CDW) (38 hospitals in Paris, France) based on the presence of an International Classification of Diseases, Tenth Revision (ICD-10) diagnostic code related to epilepsy (G40/G41) or mimickin"],"journal":["Epilepsia"],"pubmed_title":["Comparing three natural language processing methods for the automatic identification of epilepsy patients from French clinical notes."],"pmcid":["PMC12927673"],"funding_grant_id":["ANR-10-IAIHU-06","DATAE"],"pubmed_authors":["Le Gac F","Estellat C","Calonge Q","Navarro V"],"additional_accession":[]},"is_claimable":false,"name":"Comparing three natural language processing methods for the automatic identification of epilepsy patients from French clinical notes.","description":"<h4>Objective</h4>Manual review of clinical notes by experts remains the reference standard for identifying patients with epilepsy in health databases. However, this process is labor-intensive and time-consuming due to the unstructured nature of text. Prior studies have shown the potential of natural language processing for automated phenotyping. We aim to develop and validate algorithms capable of identifying patients with epilepsy based on a set of clinical notes.<h4>Methods</h4>A population of 109 448 patients was selected from the Assistance Publique-Hôpitaux de Paris (AP-HP) Clinical Data Warehouse (CDW) (38 hospitals in Paris, France) based on the presence of an International Classification of Diseases, Tenth Revision (ICD-10) diagnostic code related to epilepsy (G40/G41) or mimickin","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-16T18:35:38.048Z","creation":"2026-07-09T11:15:09.903Z"},"accession":"S-EPMC12927673","cross_references":{"pubmed":["41133988"],"doi":["10.1111/epi.18683"]}}