Full Description
This volume reviews the use of machine learning (ML) to predict antibiotic resistance in pathogens based on gene content and genome composition as data sets comprising hundreds or thousands of pathogen genomes become available. One of the main goals of this work is to promote the use of ML in front-line contexts while simultaneously emphasizing the additional improvements that are required to use these techniques in a secure and confident manner. Given the variety of quantitative and qualitative laboratory indicators of AMR, the issue of what to anticipate is not an easy one.
This book is intended for academia, students of medical science, microbiology, biology, and biotechnology, as well as experts and scientists working in the fields of infectious diseases, government health organizations, and medicine.
Contents
Preface. Role of Artificial Intelligence in Fighting Antimicrobial Resistance. Artificial Intelligence in Disease Surveillance and Monitoring Resistance Microbes. Artificial Intelligence in Antibiotic Discovery and Development. Artificial Intelligence for Precision Medicine in Antimicrobial Resistance. Artificial Intelligence-driven Diagnostics for Antimicrobial Resistance. Artificial Intelligence-Enabled Antimicrobial Stewardship Programs. Big Data Analytics and Antimicrobial Resistance. Artificial Intelligence in Veterinary Medicine and Antimicrobial Resistance. Ethical and Regulatory Considerations for Artificial Intelligence in Managing AMR. Index.
