Application of transformers for predicting epilepsy treatment response

Jiun Choong, Haris Hakeem, Zhibin Chen, Martin Brodie, Nicholas Lawn, Tom Drummond, Patrick Kwan, Zongyuan Ge

Research output: Other contributionResearch

Abstract

There 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 (74.79, respectively) in the validation cohort.Competing Interest StatementThe authors have declared no competing interest.Funding StatementDid not receive any specific fundingAuthor DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study protocol was ruled exempt by the institutional review broad of Western Infirmary in Glasgow, Scotland. Patient consent was waived because all data were deidentified prior to analysis. The study was approved by the Royal Perth Hospital Human Research Ethics Committee (Reference number: EC 2009/054) and registered with the Monash University Human Research Ethics Committee (project number: 12131). All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesData can be provided to qualified investigators with institutional regulations.
Original languageEnglish
Typepreprint
Media of outputmedRxiv
PublishermedRxiv
DOIs
Publication statusPublished - 13 Nov 2020

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