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Direct Uncertainty Prediction for Medical Second Opinions
Maithra Raghu · Katy Blumer · Rory sayres · Ziad Obermeyer · Bobby Kleinberg · Sendhil Mullainathan · Jon Kleinberg

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #246

The issue of disagreements amongst human experts is a ubiquitous one in both machine learning and medicine. In medicine, this often corresponds to doctor disagreements on a patient diagnosis. In this work, we show that machine learning models can be successfully trained to give uncertainty scores to data instances that result in high expert disagreements. In particular, they can identify patient cases that would benefit most from a medical second opinion. Our central methodological finding is that Direct Uncertainty Prediction (DUP), training a model to predict an uncertainty score directly from the raw patient features, works better than Uncertainty Via Classification, the two step process of training a classifier and postprocessing the output distribution to give an uncertainty score. We show this both with a theoretical result, and on extensive evaluations on a large scale medical imaging application.

Author Information

Maithra Raghu (Cornell University / Google Brain)
Katy Blumer (Google)
Rory sayres (Google)
Ziad Obermeyer (UC Berkeley School of Public Health)
Bobby Kleinberg (Cornell)
Sendhil Mullainathan (Harvard University)
Jon Kleinberg (Cornell University)

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