Developement and validation of a ML-based online tool to predict hospital infection in real-time

Status: Data Acquision

This project focuses on developing a machine learning-based tool to predict antibiotic resistance in Hospital-Acquired Infections (HAIs) by leveraging patient data from the MIMIC-IV dataset. Using Python, a supervised machine learning model is developed to analyze patient demographics, comorbidities, and lab values extracted from urine and blood cultures. This tool can be used to enhance infection control practices by providing early and accurate predictions of antibiotic resistance, thereby enabling healthcare providers to optimize treatment strategies and improve patient outcomes.

See the related incomplete github repository: https://github.com/soroushdty/antibiotic_resistance