  Robust machine learning relies on access to data that can be used with
standardized frameworks in important tasks and the ability to develop models
whose performance can be reasonably reproduced. In machine learning for
healthcare, the community faces reproducibility challenges due to a lack of
publicly accessible data and a lack of standardized data processing frameworks.
We present MIMIC-Extract, an open-source pipeline for transforming raw
electronic health record (EHR) data for critical care patients contained in the
publicly-available MIMIC-III database into dataframes that are directly usable
in common machine learning pipelines. MIMIC-Extract addresses three primary
challenges in making complex health records data accessible to the broader
machine learning community. First, it provides standardized data processing
functions, including unit conversion, outlier detection, and aggregating
semantically equivalent features, thus accounting for duplication and reducing
missingness. Second, it preserves the time series nature of clinical data and
can be easily integrated into clinically actionable prediction tasks in machine
learning for health. Finally, it is highly extensible so that other researchers
with related questions can easily use the same pipeline. We demonstrate the
utility of this pipeline by showcasing several benchmark tasks and baseline
results.
