MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Length-of-Stay prediction MIMIC-III Random Forests (RF) Accuracy (LOS>3 Days) 69.5% # 3
Accuracy (LOS>7 Days) 92.3 # 1
Length-of-Stay prediction MIMIC-III GRU-D Accuracy (LOS>3 Days) 68.3% # 5
Accuracy (LOS>7 Days) 91.2 # 3
Length-of-Stay prediction MIMIC-III Logistic Regression (LR) Accuracy (LOS>3 Days) 68.6% # 4
Accuracy (LOS>7 Days) 91.9 # 2


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