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 of the ML algorithms was the estimated likelihood of candidacy for resective epilepsy surgery. Model performance was assessed using 10-fold cross-validation.
There were 5880 children (n = 137 had surgery [2.3%]) and 7604 adults with epilepsy (n = 56 had surgery [0.7%]) included in the study. Pediatric surgical patients could be identified 2.0 years (range: 0–8.6 years) before beginning their presurgical evaluation with AUC =0.76 (95% CI: 0.70–0.82) and PR-AUC =0.13 (95% CI: 0.07–0.18). Adult surgical patients could be identified 1.0 year (range: 0–5.4 years) before beginning their presurgical evaluation with AUC =0.85 (95% CI: 0.78–0.93) and PR-AUC =0.31 (95% CI: 0.14–0.48). By the time patients began their presurgical evaluation, the ML algorithms identified pediatric and adult surgical patients with AUC =0.93 and 0.95, respectively. The mean squared error of the predicted probability of surgical candidacy (Brier scores) was 0.018 in pediatrics and 0.006 in adults.
Site-specific machine learning algorithms can identify candidates for epilepsy surgery early in the disease course in diverse practice settings.