Choosing Antibiotics

Narrow example of applicaation of recommendation in healthcare

MIT CSAIL researchers claim their algorithm helps doctors pick the right antibiotics

Kyle Wiggers@Kyle_L_Wiggers in Venturebeat

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) say they’ve developed a recommendation algorithm that predicts the probability a patient’s urinary tract infection (UTI) can be treated by first- or second-line antibiotics. With this information, the model 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.

UTIs, which affect half of all women, add almost $4 billion a year in health care costs. Doctors often treat UTIs using antibiotics called fluoroquinolones, but they’ve been found to put women at risk of contracting other infections. They’re also associated with a higher risk of tendon injuries and life-threatening conditions like aortic tears, leading medical associations to issue guidelines recommending fluoroquinolones as “second-line treatments.” (A second-line treatment is a treatment for a disease employed after the initial treatment has failed, stopped working, or caused intolerable side effects.) Despite this, doctors with limited time and resources continue to prescribe fluoroquinolones at high rates. ... "