Can Deep Clinical Models Handle Real-World Domain Shifts?
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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, thereby leading to community-wide data curation efforts. However, due to the 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, particularly when there are systematic domain shifts not known a priori. In this paper, we formalize these challenges by emulating a large class of domain shifts that can occur in clinical settings, and argue that evaluating the behavior of predictive models in light of those shifts is an effective way of quantifying the reliability of clinical models. More specifically, we develop an approach for building challenging scenarios, based on analysis of disease landscapes, and utilize unsupervised domain adaptation to compensate for the domain shifts. Using the openly available MIMIC-III EHR dataset for phenotyping, we generate a large class of scenarios and evaluate the ability of deep clinical models in those cases. For the first time, our work sheds light into data regimes where deep clinical models can fail to generalize, due to significant changes in the disease landscapes between the source and target landscapes. This study emphasizes the need for sophisticated evaluation mechanisms driven by real-world domain shifts to build effective AI solutions for healthcare.
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