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  • [music]

  • One of the things I'm most concerned about is actually this question of fairness in machine learning.

  • [music and background talking]

  • My name is Irene Chen.

  • I am a third-year PhD student at MIT in Electrical Engineering and Computer Science.

  • My advisor is Professor David Sontag in the Clinical Machine Learning Group.

  • Healthcare in particular has a long history

  • of having disparate impact on different groups.

  • So with machine learning we have access to a lot more people's data,

  • people have more voices, people get more represented in the machine learning models

  • that we could then potentially roll out.

  • [music]

  • I am working on heart failure specifically.

  • Heart failure is a chronic condition

  • where after diagnosis people live on average about five years.

  • We want to be able to determine

  • what kinds of treatments will have better outcomes than other treatments

  • based on who you are your age your past history, your lab results, all of that.

  • And then there's also different types of heart failure out there

  • so we're trying to figure out,

  • "Are the types that we think are out there even the same types that there should be?

  • or it be slightly different classifications?"

  • We have really close collaborations with hospitals in the downtown Boston area.

  • And they have allowed us to have access to a lot of their electronic health records.

  • So every time a patient comes in with heart failure diagnosis

  • we can actually pull all the other times they've come to the hospital

  • and maybe they didn't know that they had heart failure

  • and we can see, "Oh, three years ago you had this abnormal lab result

  • "and we maybe didn't think it was anything but now, knowing what we know now

  • we can pull it all together and build this patient trajectory.

  • And then using your patient trajectory and then all these other patient trajectories,

  • when a new person comes in, we can build potentially a patient trajectory for them as well."

  • [music]

  • Health care machine learning has been going on for a long time.

  • It is only recently though that we have access to all this data

  • because a lot of hospitals are now digitized.

  • So with this increased amount of data

  • we can now start to take some of the techniques that people worked on in the 80's and 90's and 2000's

  • and see does it work on five million patients

  • instead of the few hundred you could sort of cobble together and hand label.

  • Now, we have much larger datasets, much more powerful computing powers,

  • that we can bring it all together and do really great health care for machine learning research.

  • [music]


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B1 US machine learning heart failure machine patient failure learning

The heart of the matter

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    jbsatvtac1 posted on 2019/08/22
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