{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T03:33:44Z","timestamp":1791862424575,"version":"4.3.4"},"reference-count":45,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>The increasing implementation of electronic health records allows the use of advanced text\u2010mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used a document\u2010based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2\u2009years and a follow\u2010up after the age of 3\u2009years (\u201cDS cohort,\u201d 56 individuals). We selected two control populations, a \u201cgeneral control cohort\u201d (275 individuals) and a \u201cneurological control cohort\u201d (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome\u2010wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2\u2009years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in\u2010hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non\u2010seizure\u2010related concepts emerged after the age of 2\u20133\u2009years, referring to the nonseizure comorbidities classically associated with DS.<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of \u201clongitudinal phenotyping\u201d could be applied to other rare and very rare conditions with poor natural history description.<\/jats:p><\/jats:sec>","DOI":"10.1111\/epi.17855","type":"journal-article","created":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T03:40:22Z","timestamp":1702093222000},"page":"350-361","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome"],"prefix":"10.1111","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3103-230X","authenticated-orcid":false,"given":"Tommaso","family":"Lo\u00a0Barco","sequence":"first","affiliation":[{"name":"Department of Pediatric Neurology, Necker\u2010Enfants Malades Hospital, Assistance Publique\u2013H\u00f4pitaux de Paris, Reference Center for Rare Epilepsies, Member of European Reference Network EpiCARE Universit\u00e9 Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Garcelon","sequence":"additional","affiliation":[{"name":"Data Science Platform, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale Unit\u00e9 Mixte de Recherche 1163, Imagine Institute Universit\u00e9 Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Neuraz","sequence":"additional","affiliation":[{"name":"Data Science Platform, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale Unit\u00e9 Mixte de Recherche 1163, Imagine Institute Universit\u00e9 Paris Cit\u00e9  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5877-4074","authenticated-orcid":false,"given":"Rima","family":"Nabbout","sequence":"additional","affiliation":[{"name":"Department of Pediatric Neurology, Necker\u2010Enfants Malades Hospital, Assistance Publique\u2013H\u00f4pitaux de Paris, Reference Center for Rare Epilepsies, Member of European Reference Network EpiCARE Universit\u00e9 Paris Cit\u00e9  Paris France"},{"name":"Translational Research for Neurological Disorders, Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale Unit\u00e9 Mixte de Recherche 1163, Imagine Institute Universit\u00e9 Paris Cit\u00e9  Paris 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