A Computational Approach to Epilepsy Treatment: An AI-optimized Global Natural Product Prescription System

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 computationa l 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 - e fficacy 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), A yurveda, and ethnopharmacological d atabases, integrating their bioactive properties with clinical outcomes from 48 RCTs covering 48 epilepsy conditions (n=5,216). 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 s eizure frequency reduction with AI - optimized herbal prescriptions compared to conventional protocols (95 \ % CI: 18.7 - 37.3 \ %, p=0.003). Keywords: epilepsy, herbal medicine, computational pharmacology, AI - optimized prescription, natural products, machine learning, precision medicine, Bayesian optimization, clinical validation Introduction Despite being among the most difficult to treat neurological disorders (W orld Health Organization: WHO, 2024), it is estimated by the W orld Health Organization that there are close to 50 million people living with epilepsy (Figure 1A: Global Epilepsy Prevalence and Treatment Gaps).