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[CLICK] [SQUEAK] [PAGES RUSTLING] [MOUSE DOUBLE-CLICKS] PROFESSOR: So today we'll be continuing along the theme of risk stratification. I'll spend the first half to 2/3 of today's lecture continuing where we left off last week before the discussion. I'll talk about how does one derive the labels that one uses within a supervised machine learning approach. I'll continue talking about how one evaluates risk stratification models. And then I'll talk about some of the subtleties that arise when you want to use machine learning for health care, specifically for risk stratification. And I think that's going to be one of the most interesting parts of today's lecture. In the last third of today's lecture, I'll be talking about how one can rethink the supervised machine learning problem, not to be a classification problem, but be something closer to a regression problem. And one now thinks about not will someone, for example, develop diabete...

[CLICK] DAVID SONTAG: So welcome to spring 2019 Machine Learning for Healthcare. My name is David Sontag. I'm a professor in computer science. Also I'm in the Institute for Medical Engineering and Science. My co-instructor today will be Pete Szolovits, who I'll introduce more towards the end of today's lecture, along with the rest of the course staff. So the problem. The problem is that healthcare in the United States costs too much. Currently, we're spending $3 trillion a year, and we're not even necessarily doing a very good job. Patients who have chronic disease often find that these chronic diseases are diagnosed late. They're often not managed well. And that happens even in a country with some of the world's best clinicians. Moreover, medical errors are happening all of the time, errors that if caught, would have prevented needless deaths, needless worsening of disease, and more. And healthcare impacts all of us. So I imagine that almo...