Machine-learning-assisted selection of antibiotic prescription

Recent years have seen a worrying increase in the levels of antibiotic resistance of many bacterial infections. Antibiotic resistance not only makes treating bacterial infections difficult but also decreases the effectiveness of antibiotic prophylaxis needed for safe surgeries, organ transplantation and cancer treatment1. There is an urgent need for new effective antibiotics; however, the antibiotic-development pipeline is dry. Without government intervention, research to develop new antibiotics is rarely profitable, and consequently most major pharmaceutical companies have left the field1. Therefore, using the antibiotics that we have at our disposal in an optimized way is crucial, to avoid the risks of both treatment failure and further increasing resistance levels2. In this issue of Nature Medicine, Yelin et al.3 describe a strategy for combating the drug resistance caused by mismatched antibiotic prescriptions in urinary tract infections (UTIs)