Implementation of AI for predicting antibiotic resistance patterns: A hospital-based study

TL;DR

This study explores using AI to predict antibiotic resistance in hospitals, showing that AI can improve antibiotic use and patient care.

Contribution

The novel contribution is implementing AI models to predict antibiotic resistance using clinical and microbial data in a hospital setting.

Findings

The Random Forest model achieved the highest accuracy, precision, and recall in predicting antibiotic resistance.

AI integration in clinical workflows can enhance antibiotic stewardship and improve patient outcomes.

Machine learning models show promise in addressing antibiotic resistance challenges in healthcare.

Abstract

The use of Artificial Intelligence (AI) to predict antibiotic resistance patterns in a hospital setting is of interest. By leveraging
machine learning (ML) models, including Random Forest, Logistic Regression and Support Vector Machines, the study aimed to predict
resistance based on patient demographics, microbial species and clinical data. The Random Forest model outperformed other models in
terms of accuracy, precision and recall. Data shows the importance of integrating AI-driven tools into clinical workflows for improved
antibiotic stewardship and patient outcomes. Despite challenges, AI presents a promising approach for combating antibiotic resistance in
healthcare.