A Computational Approach to Epilepsy Treatment: An AI-optimized Global Natural Product Prescription System
This addresses epilepsy treatment for patients seeking alternative medicine, offering a computational approach to optimize natural product prescriptions, though it is incremental in applying existing AI methods to this domain.
The study tackled the problem of limited efficacy and side effects in conventional epilepsy treatments by developing an AI-optimized system for herbal prescriptions, resulting in a 28.5% greater seizure frequency reduction compared to conventional protocols in a validation trial.
Epilepsy is a prevalent neurological disease with millions of patients worldwide. Many patients have turned to alternative medicine due to the limited efficacy and side effects of conventional antiepileptic drugs. In this study, we developed a computational approach to optimize herbal epilepsy treatment through AI-driven analysis of global natural products and statistically validated randomized controlled trials (RCTs). Our intelligent prescription system combines machine learning (ML) algorithms for herb-efficacy characterization, Bayesian optimization for personalized dosing, and meta-analysis of RCTs for evidence-based recommendations. The system analyzed 1,872 natural compounds from traditional Chinese medicine (TCM), Ayurveda, and ethnopharmacological databases, integrating their bioactive properties with clinical outcomes from 48 RCTs covering 48 epilepsy conditions (n=5,216). Using LASSO regression and SHAP value analysis, we identified 17 high-efficacy herbs (e.g., Gastrodia elata [using é for accented characters], Withania somnifera), showing significant seizure reduction (p$<$0.01, Cohen's d=0.89) with statistical significance confirmed by multiple testing (p$<$0.001). A randomized double-blind validation trial (n=120) demonstrated 28.5\% greater seizure frequency reduction with AI-optimized herbal prescriptions compared to conventional protocols (95\% CI: 18.7-37.3\%, p=0.003).