Update README.md

README.md

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     <img src="https://github.com/VectorInstitute/odyssey/assets/90617686/34ecf262-e455-4866-a870-300433d09bfe" width="50%">
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+<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>
 
-https://vectorinstitute.github.io/EHRMamba/
-
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-A library for developing foundation models using Electronic Health Record (EHR) data.
+<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:
+
+1. **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.
+
+2. **models**:
+   - Implements models including XGBoost, LSTM, CEHR-BERT, BigBird, MultiBird, and EHRMamba.
+   - Offers various embedding classes necessary for the models.
+
+3. **evals**:
+   - Includes tools for testing models on clinical prediction tasks and forecasting.
+   - Provides evaluation metrics for thorough assessment of model performance.
+
+4. **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}
+}
+```
+