Direct Uncertainty Prediction for Medical Second Opinions

摘要

The issue of disagreements amongst human experts is a ubiquitous one in both machine learning and medicine. 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 featu...更多
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2018.

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