AMR Assist

Title: Machine Learning Approach for Personalized Antibiotic Efficacy Prediction in the Indian Demographic Background & Summary: This research is being conducted as an academic capstone project to develop a Clinical Decision Support System (CDSS) using Machine Learning. The objective is to build a predictive model (utilizing algorithms such as XGBoost or Random Forest) that analyzes historical susceptibility data to predict the probability of antibiotic resistance in a patient context. How this research helps: 1. Strengthen Stewardship: By predicting "Resistant" vs. "Sensitive" outcomes before prescription, the model aims to reduce the prescription of ineffective antibiotics. 2. Public Health: The study focuses on analyzing regional resistance trends (specifically within India/Asia) to understand how demographic factors influence drug efficacy. 3. Educational Value: This project serves to demonstrate the application of Data Science in mitigating the global threat of Antimicrobial Resistance (AMR). The data requested will be used strictly for training and validating the model to ensure accurate, real-world applicability compared to synthetic datasets.