Antibiotics are a key weapon in the battle against countless diseases, but one downside is they frequently have a tendency to be less effective the more often they’re used.

A recent study, for example, noted that the question of antibiotic effectiveness is particularly important with disease such as urinary tract infections (UTIs), which according to the WHO affect half of all women and, in this country alone, add almost $4 billion a year in unnecessary healthcare costs.

In the study, which was published in Science Translational Medicine, a team of MIT 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.”

The team said that a system like this could be used when a patient comes into the emergency room or their primary physician’s office with a suspected UTI.

“With this algorithm we can actually ask the doctor what specific probability of treatment failure they’re willing to risk in order to reduce the use of second-line drugs by a certain amount,” said Sanjat Kanjilal, a Harvard Medical School lecturer, infectious diseases physician and associate medical director of microbiology at the BWH and a co-author of the report.

The project is “part of a larger wave of machine learning models that have been used to predict antibiotic resistance in infectious syndromes such as bloodstream infections and using pathogen genomic data.”

“What’s exciting about this research is that it presents a blueprint for the right way to do retrospective evaluation,” said David Sontag, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a faculty member in the Institute for Medical Engineering and Science (IMES) “We do this by showing that one can do an apples-to-apples comparison within the existing clinical practice. When we say we can reduce second-line antibiotic use and inappropriate treatment by certain percentages, we have confidence in those numbers relative to clinicians.”

Moving forward, the researchers intend to focus their efforts on conducting a randomized controlled trial comparing usual practice to algorithm supported decisions. They also aim to increase the diversity of their sample size to improve the algorithm’s recommendations across race, ethnicity, socioeconomic status, and more complex health backgrounds.