Machine learning reduces use of riskier antibiotics for Urinary Tract Infections

How machine learning is going to revolutionize medicine! Remember: This is only the beginning ...

"Because of its ubiquity, one topic that’s particularly concerning is urinary tract infections (UTIs), which affect half of all women and add almost $4 billion a year in unnecessary health-care costs. Doctors often treat UTIs using antibiotics called fluoroquinolones that are inexpensive and generally effective. However, they have also been found to put women at risk of becoming infected with other difficult-to-treat bacteria, such as C. difficile and certain species of Staphylococcus, and also to increase their risk of tendon injuries and life-threatening conditions like aortic tears. ...
In a new paper, the researchers present a recommendation algorithm that predicts the probability that a patient’s UTI can be treated by first- or second-line antibiotics. With this information, the model then makes a recommendation for a specific treatment that selects a first-line agent as frequently as possible, without leading to an excess of treatment failures. ... "

Algorithm reduces use of riskier antibiotics for UTIs | MIT News | Massachusetts Institute of Technology An MIT recommendation algorithm predicts the probability that a patient’s specific UTI can be treated by different antibiotics, minimizing the need to use particular antibiotics that may have negative side effects.

Here is the underlying research paper:

A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection