Machine Learning and Artificial Intelligence in Combating Multidrug-Resistant Bacterial Infections

This book explores how artificial intelligence and machine learning can help solve one of healthcare’s most critical challenges: multidrug-resistant bacterial infections. Covering AI/ML techniques, resistance mechanisms, diagnostics, drug discovery, and case studies, it offers a multidisciplinary approach combining microbiology, bioinformatics, and data science. Beginning with foundational AI/ML methods, including supervised and unsupervised learning, deep learning, and neural networks, it explores how these tools can analyse complex biological datasets and identify patterns that traditional approaches often miss. With real-world case studies and discussion of ethical and regulatory issues, it provides a practical and forward-looking guide to leveraging AI/ML for combating MDR pathogens. The book:Covers key algorithms and their relevance to healthcare, including deep learning and neural networks. Explains genetic, biochemical, and microbial factors that contribute to antibiotic resistance. Shows how machine learning models can rapidly identify MDR infections and support early interventions. Include details of AI-driven screening, compound repurposing, and the design of antimicrobial peptides. Explores how genomics, proteomics, and metabolomics are analyzed using AI/ML to reveal new targets, highlights real-world AI solutions in diagnostics, treatment, and drug development. Discusses algorithmic bias, data privacy, and regulatory considerations in clinical AI applications. This book is essential for researchers, healthcare professionals, data scientists, and graduate students in computer science, microbiology, pharmacology, and computational biology who are working to combat multidrug-resistant infections through interdisciplinary innovation.