Predictive Analytics in AMR Forecasting Resistance Patterns and Guiding Treatment Decisions

Abstract

In the current healthcare setting, antimicrobial resistance (AMR) poses a pressing challenge that could undermine both patient safety and medical improvements (World Health Organization [WHO], 2023; Laxminarayan et al., 2020). The chapter examines the varied contributions of predictive analytics to the combat against AMR, emphasising its potential to alter treatment tactics and better patient results. Initially, we study the current situation of AMR, focusing on its origins, which include the oversupply of antibiotics, unfinished treatment plans, and poor infection control methods (Centers for Disease Control and Prevention (CDC), 2022; Ventola, 2015). The chapter points out the value of applying predictive analytics as a preventive method in the management of AMR. By using extensive datasets, statistical algorithms, and machine learning techniques, predictive analytics is able to project resistance patterns, recognize patients at high risk, and influence effective antimicrobial stewardship (Kumar et al., 2023; Wang et al., 2022).