This library was developed in order to facilitate rapid prototyping in Python of predictive machine-learning models using longitudinal medical data from an OMOP CDM-standard database. omop-learn supports the easy definition of predictive clinical tasks, featurizations of OMOP data, and cohorts of relevance. We further provide methods using sparse tensor implementations to rapidly manipulate the collected features in the rawest form possible, allowing for dynamic transformations of the data.

Two machine-learning models are included with the library. First, a windowed linear model, which uses various backwards-facing windows to aggregate features over different timescales, then feeds these features into a regularized logistic regression model. This model was inspired by the work of Razavian et. al. '15, and despite its simplicity is often competitive with state-of-the-art algorithms. We also include SARD (Self-Attention with Reverse Distillation), a novel deep-learning algorithm that uses self-attention to allow medical events to contextualize themselves using other events in a patient's timeline. SARD also makes use of reverse distillation, a training technique we introduce that effectively initializes a deep model using a high-performing linear proxy, in this case the windowed linear model described above.

We described the details of this method and the SARD architecture in our paper Kodialam et al. AAAI '21. If you use omop-learn in your work, please cite our paper:

Kodialam, Rohan, et al. "Deep Contextual Clinical Prediction with Reverse Distillation." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 1. 2021.

BibTeX:

@inproceedings{kodialam2021deep,
  title={Deep Contextual Clinical Prediction with Reverse Distillation},
  author={Kodialam, Rohan and Boiarsky, Rebecca and Lim, Justin and Sai, Aditya and Dixit, Neil and Sontag, David},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={1},
  pages={249--258},
  year={2021}
}

Dependencies for omop-learn are managed through an environment.yml file. Run the following from the current directory to create the conda environment necessary to run the package:

conda env create -f environment.yml
conda activate omop-learn
pip install .

This installs the dependencies and pip installs the omop-learn package into a conda environment omop-learn.

For a more detailed summary of omop-learn's data collection pipeline, and for documentation of functions, please see the full documentation for this repo, which also describes the process of creating one's own cohorts, predictive tasks, and features.

omop-learn was written by Rohan Kodialam and Jake Marcus, with additional contributions by Rebecca Boiarsky, Justin Lim, Ike Lage, Shannon Hwang, Hunter Lang, Christina Ji, Irene Chen, and Alejandro Buendia.

This package was developed as part of a collaboration with Independence Blue Cross and would not have been possible without the advice and support of Aaron Smith-McLallen, Ravi Chawla, Kyle Armstrong, Luogang Wei, Neil Dixit, and Jim Denyer.