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

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. The aim is to identify language patterns that may contain useful clinical information that is not overtly considered by the clinician from first-visit documentation to improve the early diagnosis of childhood epilepsy.

Methods

We analyzed 1561 patient letters from the first two seizure clinics. The dataset was divided into training and test sets to evaluate performance and generalizability. We employed an established Naïve Bayes model as a natural language processing technique and a sentence-embedding (l

SUBMITTER: Loyens J 

PROVIDER: S-EPMC12747707 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature