Mediation Toxicity for Casting Using Deep Learning

the main goal of this work is to predict medication toxicity with the help of deep learning models and consider opioids prescriptions only. As the dataset used has 25k records and 256 features, Recurrent Neural Networks (RNN), Random Forest, XGBoost, Voting Classifier increase the chances of prediction. To enhance the feature space in the dataset, various features regarding medications that are frequently prescribed include Acetaminophen, Gabapentin, and Levothyroxine among others; the target feature is known as the ‘Opioid Prescriber’. With the goal of detecting whether a prescriber is subscribing opioids and to minimize the threats related tole opioid toxicity, this work tries to ascertain associative structures among distinct medications. The integration of machine learning and deep learning will form a strong foundation, so further enhancing prescription safety and eliminating opioid toxicities.