In “NOS,” I talk with some of medicine’s most innovative thinkers to probe some of these messy unknowns behind our health care system, its players, and the stories that shape their lives. Lisa Rosenbaum: “Not otherwise specified” has always been one of my favorite phrases in medicine, not just because it’s a fancy way of saying, “We don’t really understand the root cause of something,” but also because it captures the human impulse to put tidy labels on things that remain largely unknown. So a lot of the core tasks that doctors are doing - in the emergency department, but everywhere - is the set of things that machine learning is really, really good at doing. and now, I need to estimate the probability of acute coronary syndrome, pulmonary embolus, aortic dissection, etc., etc.” So what is diagnosis but saying, “OK, I observe a physical exam, an electrocardiogram, some lab results, the way the patient’s. Diagnosis is a great machine-learning problem. Ziad Obermeyer: A lot of the things that I struggled with clinically, like figuring out, “OK, well, who is at high enough risk that they need to be in the hospital, and not out of the hospital?” That turns out to be a great machine-learning problem, because machine learning is really good at predicting a thing - so predicting who’s going to have a bad outcome on the basis of everything we see about someone now, great machine-learning problem. “Not Otherwise Specified” host Lisa Rosenbaum talks with Ziad Obermeyer about his trailblazing work in the application of machine learning to population health, the fraught problems it seeks to solve, and its lessons for critical aspects of clinical care.
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