Awesome
<p align="center"> <img src="https://github.com/VectorInstitute/odyssey/assets/90617686/34ecf262-e455-4866-a870-300433d09bfe" width="50%"> </p> <h1 style="text-align: center;">Odyssey</h1> <p style="text-align: center;">A library for developing foundation models using Electronic Health Records (EHR) data.</p> <p align="center"> <a href="https://vectorinstitute.github.io/EHRMamba/">Visit our recent EHRMamba paper</a> </p>Introduction
Odyssey is a comprehensive library designed to facilitate the development, training, and deployment of foundation models for Electronic Health Records (EHR). Recently, we used this toolkit to develop EHRMamba, a cutting-edge EHR foundation model that leverages the Mamba architecture and Multitask Prompted Finetuning (MPF) to overcome the limitations of existing transformer-based models. EHRMamba excels in processing long temporal sequences, simultaneously learning multiple clinical tasks, and performing EHR forecasting, significantly advancing the state of the art in EHR modeling. <br><br>
Key Features
The toolkit is structured into four main modules to streamline the development process:
-
data:
- Gathers EHR datasets from HL7 FHIR resources.
- Processes patient sequences for clinical tasks.
- Tokenizes data and creates data splits for model training.
- Provides a dataset class for model training.
-
models:
- Implements models including XGBoost, LSTM, CEHR-BERT, BigBird, MultiBird, and EHRMamba.
- Offers various embedding classes necessary for the models.
-
evals:
- Includes tools for testing models on clinical prediction tasks and forecasting.
- Provides evaluation metrics for thorough assessment of model performance.
-
interp:
- Contains methods for interpreting model decisions.
- Features interactive visualization of attention matrices for Transformer-based models.
- Includes novel interpretability techniques for EHRMamba and gradient attribution methods. <br><br>
Contributing
We welcome contributions from the community! Please open an issue. <br><br>
Citation
If you use EHRMamba or Odyssey in your research, please cite our paper:
@misc{fallahpour2024ehrmamba,
title={EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records},
author={Adibvafa Fallahpour and Mahshid Alinoori and Arash Afkanpour and Amrit Krishnan},
year={2024},
eprint={2405.14567},
archivePrefix={arXiv},
primaryClass={cs.LG}
}