'Article of the Year' nod to demo of machine learning for personalized medicine

"Our work was meant as a demonstration project to show how disease registries could be leveraged to yield the same sorts of insights about personalizing the longitudinal sequence of treatments as one might get from a prospective, multistage randomized trial," Krakow said in emailed comments about the work, which was based on the transplant complication known as graft-vs.-host When a cancer patient has a complex medical history, a little-studied disease and many potential treatment options, published guidelines and studies rarely indicate one clear treatment choice. So in everyday clinical practice, physicians recommend treatments to their patients based on an amalgam of published and anecdotal evidence, and patient-specific characteristics and preferences. Then, they adjust treatment as needed as time goes on. In what Krakow calls "algorithm-informed treatment," a computer would generate a treatment recommendation for a specific time point in an individual patient's therapeutic course.