Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy.

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Bibliographic Details
Title: Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy.
Authors: Hung PL; Division of Pediatric Neurology, Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan; Rare Childhood Neurologic Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan., Chen JP; Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan., Lin TP; Department of Electrical Engineering, National Central University, Taoyuan, Taiwan., Hsieh TY; Division of Pediatric Neurology, Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan; Rare Childhood Neurologic Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan., Chen YF; Division of Pediatric Neurology, Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan; Rare Childhood Neurologic Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan., Su TY; Division of Pediatric Neurology, Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan; Rare Childhood Neurologic Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan., Peng SJ; In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan. Electronic address: sjpeng2019@tmu.edu.tw.
Source: Pediatric neurology [Pediatr Neurol] 2026 May; Vol. 178, pp. 130-137. Date of Electronic Publication: 2026 Feb 19.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Science Publishing Country of Publication: United States NLM ID: 8508183 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-5150 (Electronic) Linking ISSN: 08878994 NLM ISO Abbreviation: Pediatr Neurol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-5150
DOI:10.1016/j.pediatrneurol.2026.02.013