Machine learning for assessment of antimicrobial susceptibility: A novel bioinformatics approach against antimicrobial resistance

Abstract

Antimicrobial resistance (AMR) is one of the growing challenges of modern medicine, which causes thousands of deaths worldwide annually [1, 2]. To treat microbial infections, health specialists must prescribe effective antibiotics to patients on time. Microbiology laboratories should perform antimicrobial susceptibility tests on bacteria isolated from patients to select appropriate drugs [3]. Rapid detection of bacteria and their pattern of sensitivity to antibiotics play an essential role in the treatment of microbial infections [3]. Conventional antibiotic susceptibility testing method including disk diffusion is culture-based and take a long time (usually about 24 hours) to determine suitable drugs prescription [4]. In acute infectious diseases, physicians usually immediately begin experimental treatment including possible antibiotic therapy with high-sure therapeutic probability potential based on the patient's clinical symptoms. Improper administration or overuse of antibiotics could lead to the release of bacterial toxins such as bacterial lipopolysaccharide, the spread of bacteria, increased antibiotic resistance, and even death [3]. Therefore, one of the main challenges in detecting microbial resistance is obtaining a reliable antibiotic susceptibility pattern in several minutes or hours via simple and inexpensive tools [4].