How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review

Current epilepsy management protocols often depend on anti-seizure medication (ASM) trials and assessment of clinical response. This may delay the initiation of the ASM regimen that might optimally balance efficacy and tolerability for individual patients. Machine learning (ML) can offer a promising tool for efficiently predicting ASM response.

The objective of this review is to synthesize the available information about the effectiveness and limitations of ML models in predicting and classifying the response of patients with epilepsy to ASMs, and to assess the impact of various data inputs on prediction performance.

We conducted a comprehensive search of studies utilizing ML models for ASM response prediction using PubMed and Scopus up until November 2024.

The review included 37 studies. Various data types, including clinical information, brain MRI, EEG, and genetic data, are useful in predicting responses to ASMs. Tree-based ML algorithms and Support Vector Machines are the most used models. Reported results vary widely, with certain models achieving near-perfect accuracy and others performing similar to random classifiers. The review also highlights the limitations of this research field, especially concerning the quality and quantity of data.

The findings indicate that while ML models show great promise in predicting ASM responses in epilepsy, further research is required to refine these models for practical clinical application. The review underscores both the potential of ML in advancing precision medicine in epilepsy management and the need for continued research to improve prediction accuracy.