{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:58:40Z","timestamp":1768571920293,"version":"3.49.0"},"posted":{"date-parts":[[2025,11,6]]},"group-title":"Neurology","reference-count":54,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,11,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Acquired epilepsy is a disabling, potentially preventable complication of acute brain injury (ABI). Yet, it remains a leading cause of new onset epilepsy in adults. Acquired epilepsy is a particularly challenging subtype of epilepsy to identify, as the ABI and its acute consequences can confound the later diagnosis of acquired epilepsy. We identified a retrospective cohort of patients with ABI (N=828) and optimized a general epilepsy algorithm to extract relevant keywords. We confirmed that applying a broad epilepsy phenotyping algorithm to a high risk, complex population like acute brain injured patients results in a high number of false positives. We developed multivariate models to identify ABI-acquired epilepsy 1) at the patient level using temporal trends, and 2) the note-level using keywords. Our models achieved high performance in both internal and external validation cohorts. Note-level re-classification also allowed for an estimation of time to epilepsy onset. This work enables large-scale, retrospective studies of ABI-acquired epilepsy across sites. Large-scale implementation may provide insights into acquired epilepsy epidemiology, identification of novel epilepsy risk factors and ultimately new treatments.<\/jats:p>","DOI":"10.1101\/2025.11.04.25339484","type":"posted-content","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T20:20:19Z","timestamp":1762460419000},"source":"Crossref","is-referenced-by-count":0,"title":["From Clinical Narrative to Diagnosis: Scalable Identification of Acquired Epilepsy"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9339-0798","authenticated-orcid":false,"given":"Justin R.","family":"Wheelock","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7203-2832","authenticated-orcid":false,"given":"Marta B.","family":"Fernandes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. 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