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Welcome to Cardea

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This library is under development. Please contact dai-lab@mit.edu or any of the contributors for more information.

Date: May 27, 2021 Version: 0.1.3.dev0

Overview

This library is under development. Please contact dai-lab@mit.edu or any of the contributors for more information.

Cardea is a machine learning library built on top of schemas that support electronic health records (EHR). The library uses a number of AutoML tools developed under The Human Data Interaction Project at Data to AI Lab at MIT.

Our goal is to provide an easy to use library to develop machine learning models from electronic health records. A typical usage of this library will involve interacting with our API to develop prediction models.

Cardea Process

A series of sequential processes are applied to build a machine learning model. These processes are triggered using our following APIs to perform the following:

  • loading data using the automatic data assembler, where we capture data from its raw format into an entityset representation.

  • data labeling where we create label times that generates (1) the time index that indicates the timespan for which I create my features (2) the encoded labels of the prediction task. this is essential for our feature engineering phase.

  • featurization for which we automatically feature engineer our data to generate a feature matrix.

  • lastly, we build, train, and tune our machine learning model using the modeling component.