Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

ABSTRACT:

Objectives

Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery.

Materials & methods

In this multicenter, retrospective, longitudinal cohort study, ML algorithms were trained on n-grams extracted from free-text neurology notes, EEG and MRI reports, visit codes, medications, procedures, laboratories, and demographic information. Site-specific algorithms were developed at two epilepsy centers: one pediatric and one adult. Cases were defined as patients who underwent resective epilepsy surgery, and controls were patients with epilepsy with no history of surgery. The output

SUBMITTER: Wissel BD 

PROVIDER: S-EPMC8178229 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature