PhysioPro

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A deep learning framework for physiological data processing and understanding.

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A deep learning framework for physiological data processing and understanding.

## NEWS

- [NEW🔥] Pretrained checkpoint for paper `Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling` is now available [here](https://seqml.github.io/MMM/).
- `Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling` is now available [here](docs/MMM.md).
- `ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling` is now available [here](docs/contiformer.md).

## Installation

To install from source code:
```bash
pip install .
```

To install in development mode:
```bash
pip install --editable .[dev]
```

## Quick Start

See [quick start](docs/quick-start.md) for a quick start guide.

## Responsible AI Transparency Information

An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Creating a system that is fit for its intended purpose requires an understanding of how the technology works, its capabilities and limitations, and how to achieve the best performance. Microsoft has a broad effort to put our AI principles into practice. To find out more, see [Responsible AI principles from Microsoft](https://www.microsoft.com/en-us/ai/responsible-ai).

### Use of This Code

Our goal in publishing this code is to facilitate AI research on time-series data, especially in the field of physiology.

This code should not be used in clinical settings to influence treatment decisions.

### Project Data and Models

We do not provide any data or trained models with this project. Users need to train models with their own data.

### Fairness and Responsible AI Testing

At Microsoft, we strive to empower every person on the planet to do more. An essential part of this goal is working to create technologies and products that are fair and inclusive. Fairness is a multi-dimensional, sociotechnical topic and impacts many different aspects of our work.

When systems are deployed, Responsible AI testing should be performed to ensure safe and fair operation for the specific use case. No Responsible AI testing has been done to evaluate this method including validating fair outcomes across different groups of people. Responsible AI testing should be done before using this code in any production scenario.

> Note: The documentation included in this ReadMe file is for informational purposes only and is not intended to supersede the applicable license terms.

## Code of Conduct

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

## License

Licensed under the [MIT](LICENSE) license.

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