  An estimated 180 papers focusing on deep learning and EHR were published
between 2010 and 2018. Despite the common workflow structure appearing in these
publications, no trusted and verified software framework exists, forcing
researchers to arduously repeat previous work. In this paper, we propose
Cardea, an extensible open-source automated machine learning framework
encapsulating common prediction problems in the health domain and allows users
to build predictive models with their own data. This system relies on two
components: Fast Healthcare Interoperability Resources (FHIR) -- a standardized
data structure for electronic health systems -- and several AUTOML frameworks
for automated feature engineering, model selection, and tuning. We augment
these components with an adaptive data assembler and comprehensive data- and
model- auditing capabilities. We demonstrate our framework via 5 prediction
tasks on MIMIC-III and Kaggle datasets, which highlight Cardea's human
competitiveness, flexibility in problem definition, extensive feature
generation capability, adaptable automatic data assembler, and its usability.
