Time-Series based Drug Efficiency and Side-Effect Prediction: Leveraging Machine Learning for Enhanced Drug Safety and Personalized Medicine

Predicting drug side effects and efficacy is a critical challenge in modern medicine with significant implications for patient safety and treatment optimization. This study introduces a hybrid machine learning framework that combines Random Forest for static feature analysis and LSTM (Long Short-Term Memory) for time-series analysis. The model leverages data on patient demographics, drug properties, intake schedules, and feedback to predict sideeffect intensity and assess drug efficiency trends over time. Random Forest handles static features like patient-specific attributes and drug properties, while LSTM captures temporal dependencies in drug responses, enabling predictions of drug effectiveness across sequential timeframes. By integrating these models, the framework offers actionable insights, such as the likelihood and severity of side effects and time-series trends in drug efficiency, providing a comprehensive solution for personalized medicine. Validated by metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), this approach demonstrates high accuracy and holds significant potential for improving healthcare outcomes through predictive analytics, enabling data-driven treatment adjustments, minimizing adverse reactions, and optimizing therapeutic efficacy..