<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Park KI</submitter><funding>Korea Health Industry Development Institute</funding><pagination>33177</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12475458</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>15(1)</volume><pubmed_abstract>Despite the increasing number of available antiseizure medications (ASMs), optimal medical therapy is still a process of trial and error. We aimed to predict the responsiveness of various ASMs based on initial tests using artificial intelligence. The study consisted of 2586 patients fulfilling the following criteria: (1) first visit to the epileptologists from 2008 to 2017, (2) a diagnosis of epilepsy, and 3) ≥ three years of follow-up duration. The clinical characteristics, ASM history, seizure frequency, laboratory, EEG, and MRI results, were collected. Machine algorithms were utilized to predict the responsiveness of specific regimens. Valproate showed the highest area under curve (AUC), 0.636. The AUCs of levetiracetam, oxcarbazepine, and lamotrigine were 0.614, 0.633, and 0.674. The A</pubmed_abstract><journal>Scientific reports</journal><pubmed_title>Prediction of personalized antiseizure medications response based on clinical signatures in epilepsy.</pubmed_title><pmcid>PMC12475458</pmcid><funding_grant_id>RS-2023-00265638</funding_grant_id><pubmed_authors>Lee SK</pubmed_authors><pubmed_authors>Park KI</pubmed_authors><pubmed_authors>Hwang S</pubmed_authors><pubmed_authors>Jung KH</pubmed_authors><pubmed_authors>Kim YG</pubmed_authors><pubmed_authors>Lee SB</pubmed_authors><pubmed_authors>Kim YJ</pubmed_authors><pubmed_authors>Chu K</pubmed_authors><pubmed_authors>Lee ST</pubmed_authors><pubmed_authors>Jung KY</pubmed_authors><pubmed_authors>Shin Y</pubmed_authors><pubmed_authors>Moon J</pubmed_authors><pubmed_authors>Son H</pubmed_authors></additional><is_claimable>false</is_claimable><name>Prediction of personalized antiseizure medications response based on clinical signatures in epilepsy.</name><description>Despite the increasing number of available antiseizure medications (ASMs), optimal medical therapy is still a process of trial and error. We aimed to predict the responsiveness of various ASMs based on initial tests using artificial intelligence. The study consisted of 2586 patients fulfilling the following criteria: (1) first visit to the epileptologists from 2008 to 2017, (2) a diagnosis of epilepsy, and 3) ≥ three years of follow-up duration. The clinical characteristics, ASM history, seizure frequency, laboratory, EEG, and MRI results, were collected. Machine algorithms were utilized to predict the responsiveness of specific regimens. Valproate showed the highest area under curve (AUC), 0.636. The AUCs of levetiracetam, oxcarbazepine, and lamotrigine were 0.614, 0.633, and 0.674. The A</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Sep</publication><modification>2026-07-15T01:48:10.987Z</modification><creation>2026-06-27T03:17:42.668Z</creation></dates><accession>S-EPMC12475458</accession><cross_references><pubmed>41006507</pubmed><doi>10.1038/s41598-025-16881-x</doi></cross_references></HashMap>