Meditaion Toxicity Forcasting Using Deep Learning

Project Code :TCMAPY1379

Objective

This project aims to develop a deep learning-based model for predicting opioid prescription toxicity by analyzing a dataset of 25,000 records with 256 features. Using techniques like RNN, Random Forest, XGBoost, and Voting Classifiers, the goal is to identify high-risk prescribers and uncover patterns linking specific medications (e.g., Acetaminophen, Gabapentin, Levothyroxine) to increased toxicity, ultimately improving prescription safety and mitigating risks.

Abstract

Abstract

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.

Keywords: Medication Toxicity Forecasting: Deep Learning for the Prediction of Opioid Prescriber Random Forest, eXtreme Gradient Boosting, Voting Classifier Prescription Pattern Analysis: Toxicity Levels of Medication Ensemble Learning

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