Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data?

ABSTRACT:

Objective

The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome.

Methods

We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-

SUBMITTER: Eriksson MH 

PROVIDER: S-EPMC10952307 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature