Results 291 to 300 of about 1,951,229 (376)

AI language model applications for early diagnosis of childhood epilepsy based on unstructured first‐visit patient narratives: A cohort study

open access: yesEpileptic Disorders, EarlyView.
Abstract Objective Language serves as an indispensable source of information for diagnosing epilepsy, and its computational analysis is increasingly explored. This study assessed – and compared – the diagnostic value of different language model applications in extracting information.
Jitse Loyens   +5 more
wiley   +1 more source

AI powered ELT: Instructors' transformative roles and opportunities. [PDF]

open access: yesPLoS One
Almegren A   +4 more
europepmc   +1 more source

Exploring the role of apolipoprotein ε4 in progressive myoclonic epilepsy type 1

open access: yesEpileptic Disorders, EarlyView.
Abstract Objective Progressive myoclonic epilepsy type 1 (EPM1) is a neurodegenerative disease caused by biallelic variants in the cystatin B (CSTB) gene. Despite a progressive course, phenotype severity varies among patients, even within families. We studied the potential role of APOE ε4 in modifying phenotypic diversity in EPM1, given its established
Janina Gunnar   +10 more
wiley   +1 more source

Epilepsy syndromes classification

open access: yesEpilepsia Open, EarlyView.
Abstract Epilepsy syndromes are distinct electroclinical entities which have been recently defined by the International League Against Epilepsy Nosology and Definitions Task Force. Each syndrome is associated with “a characteristic cluster of clinical and EEG features, often supported by specific etiologic findings”.
Elaine C. Wirrell   +4 more
wiley   +1 more source

Simultaneous tDCS‐rTMS stimulation to regulate the language network and improve language ability in Landau–Kleffner syndrome

open access: yesEpilepsia Open, EarlyView.
Abstract Landau–Kleffner syndrome (LKS) is a rare epileptic syndrome causing language regression. In this preliminary study, we investigated the effects of simultaneous transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (rTMS) on LKS patients and the underlying mechanism based on magnetoencephalography (MEG) network ...
Runze Chen   +12 more
wiley   +1 more source

Affective Dimensions in Maternal Voice During Child Feeding in Mothers With and Without Eating Disorder History—Findings From a Machine Learning Analysis of Speech Data

open access: yesEuropean Eating Disorders Review, EarlyView.
ABSTRACT Objective Eating disorder (ED) history may impact mother‐child communication during mealtimes and contribute to transgenerational transmission of ED. This study employed machine learning (ML) to identify speech characteristics during mother‐child feeding interactions, aiming for investigating whether vocalised affective characteristics differ ...
Jana Katharina Throm   +7 more
wiley   +1 more source

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