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2018

Understanding Behavior of Clinical Models under Domain Shifts

Thiagarajan, Jayaraman J., Rajan, Deepta, Sattigeri, Prasanna

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The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare.

Deep learning, in particular, has been a game changer in building predictive models, thus leading to community-wide data curation efforts.
However, due to inherent variabilities in population characteristics and biological systems, these models are often biased to the training datasets.
This can be limiting when models are deployed in new environments, when there are systematic domain shifts not known a priori.

Understanding Behavior of Clinical Models under Domain Shifts · Around

Built on

Learning classification models from multiple experts
Hamed Valizadegan, Quang Nguyen, and Milos Hauskrecht. 2013 · 2013
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MIMIC-III, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
Earlier work this paper cites.
The information sieve. In Proceedings of the 33rd International Conference on International Conference on Machine Learning-Volume 48 . JMLR. org, 164–172
Greg Ver Steeg and Aram Galstyan. 2016 · 2016
Earlier work this paper cites.

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