Combating Antimicrobial Resistance (AMR) With Machine Learning

Abstract

Antimicrobial resistance (AMR) is a critical public health challenge, driven by excessive antibiotic use, global migration, and environmental factors. Machine learning (ML) offers promising solutions to tackle AMR by enabling the rapid identification, prediction, and treatment of resistant pathogens. This chapter explores ML methods like supervised learning, deep learning, reinforcement learning, and unsupervised learning in AMR research. Through real-world case studies, it highlights the impact of ML on personalized treatments, optimized antibiotic dosing, and novel antibiotic discovery. Challenges such as data bias, model interpretability, and clinical validation are discussed, along with future trends like federated learning and blockchain integration. ML is positioned as an interdisciplinary tool, vital for improving patient care, public health, and global cooperation in controlling AMR.