Predicting drug-resistant epilepsy — A machine learning approach based on administrative claims data

Publication date: December 2018Source: Epilepsy & Behavior, Volume 89Author(s): Sungtae An, Kunal Malhotra, Cynthia Dilley, Edward Han-Burgess, Jeffrey N. Valdez, Joseph Robertson, Chris Clark, M. Brandon Westover, Jimeng SunAbstractPatients with drug-resistant epilepsy (DRE) are at high risk of morbidity and mortality, yet their referral to specialist care is frequently delayed. The ability to identify patients at high risk of DRE at the time of treatment initiation, and to subsequently steer their treatment pathway toward more personalized interventions, has high clinical utility. Here, we aim to demonstrate the feasibility of developing algorithms for predicting DRE using machine learning methods. Longitudinal, intersected data sourced from US pharmacy, medical, and adjudicated hospital claims from 1,376,756 patients from 2006 to 2015 were analyzed; 292,892 met inclusion criteria for epilepsy, and 38,382 were classified as having DRE using a proxy measure for drug resistance. Patients were characterized using 1270 features reflecting demographics, comorbidities, medications, procedures, epilepsy status, and payer status. Data from 175,735 randomly selected patients were used to train three algorithms and from the remainder to assess the trained models' predictive power. A model with only age and sex was used as a benchmark. The best model, random forest, achieved an area under the receiver operating characteristic curve (95% confidence interval [CI]) of 0.764 (0.759, 0.770...
Source: Epilepsy and Behavior - Category: Neurology Source Type: research