Use of Predictive Analytics in Antimicrobial Resistance: A Review
Vinoth Kumar Kolluru, Yudhisthir Nuthakki, Sonika Koganti, Advaitha Naidu Chintakunta
Abstract
The growing threat of antimicrobial resistance has spurred various efforts to preserve existing antibiotics, develop new ones, combat multi-drug-resistant infections, and enhance patient outcomes. These endeavors have led to an accumulation of routinely collected data, such as electronic health records and microbiological information, which can be leveraged to develop personalized antimicrobial stewardship strategies. Machine learning techniques have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast drug susceptibility, identify epidemic patterns for surveillance, propose novel antibacterial treatments, and accelerate scientific discoveries. However, a notable gap exists between the number of machine learning applications in research and their effective implementation in practical settings. This narrative review highlights some of the remarkable opportunities that machine learning offers when applied to research related to antimicrobial resistance. In the future, machine learning tools have the potential to become a powerful weapon against superbugs. The review aims to provide an overview of available publications to assist researchers interested in exploring new approaches and to familiarize them with the current applications of machine learning techniques in this field.