Application of transformers for predicting epilepsy treatment response

Abstract

ABSTRACTThere is growing interest in machine learning based approaches to assist clinicians in treatment selection. In the treatment of epilepsy, a common neurological disorder that affects 70 million people worldwide, previous research has employed scoring methods generated from traditional machine learning methods based on pre-treatment patient characteristics to classify those with drug-resistant epilepsy (DRE). In this study, we used an attention-based approach in predicting the response to different antiseizure medications (ASMs) in individuals with newly diagnosed epilepsy. By applying a conventional transformer to model the patient’s response, we can use the predicted probability to determine the success rate of specific ASMs. Applying the transformer allowed the model to place attention on patient information and past treatments to model future drug responses. We trained a conventional transformer model based on one cohort of 1536 patients with newly diagnosed epilepsy, compared its performance with other trained models using RNN and LSTM, and applied it to a validation cohort of 736 patients. In the development cohort, the transformer model showed the highest accuracy (81%) and AUC (0.85), and maintained similar accuracy and AUC (74% and 0.79, respectively) in the validation cohort.