🏥 Personalized Drug Side-Effect Prediction using RNN

📌 Overview

Adverse Drug Reactions (ADRs) pose a significant risk in healthcare, often leading to severe health complications. This project leverages Recurrent Neural Networks (RNNs) to enhance the accuracy of drug side-effect prediction by incorporating personalized patient data such as medical history, age, and other relevant factors. The goal is to help healthcare professionals optimize prescriptions and improve patient safety.

🚀 Features

• ✅ Sequential Data Processing – RNNs excel at handling time-series medical records.
• ✅ Personalized Predictions – Considers individual patient history for better accuracy.
• ✅ Improved Drug Safety – Helps doctors anticipate ADRs more effectively.
• ✅ Optimized Treatment Decisions – Reduces risks and enhances patient care.

📊 Dataset

The dataset used for this project includes:

• Patient demographics (age, gender, medical history, etc.).
• Prescribed drug information.
• Reported side effects.
• Other relevant health parameters.

🔬 Methodology

1. Data Preprocessing: Cleaning and structuring medical records.
2. Feature Engineering: Extracting key attributes from patient history and prescriptions.
3. RNN Model Training: Leveraging deep learning techniques to predict potential ADRs.
4. Evaluation: Assessing model performance using accuracy, precision, recall, and F1-score.

🌍 Real-World Applications

• Prevention of Drug-Related Complications
• Personalized Medicine & AI-driven Treatment Plans
• Optimized Healthcare Decision Support Systems