pyoxynet 0.0.1.5

Python package of the Oxynet project (visit www.oxynet.net)

The Oxynet Python package repository

:earth_africa:
With strained healthcare systems and ageing populations, we require world-wide coordinated actions for timely diagnostics.
:hospital:
We want to contribute with sustainable approaches to more equitable health and care services.
:computer:
With Oxynet we develop automatic interpreter of cardiopulmonary exercising tests.

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Overleaf · Web app · Pypi · Docs

The Oxynet Project

There are challenges that transcend both national and continental boundaries and providing people with universal access to good quality health care is one of them. Emerging technologies in the field of AI and the availability of vast amounts of data can offer big opportunities to stimulate innovation and develop solutions.

Oxynet wants to become a tool for a quick and encompassing diagnosis of medical conditions with cardiopulmonary exercise tests (CPET) and promote accurate and timely clinical decisions, ultimately reducing the costs associated with current evaluation errors and delays.

The main building blocks of Oxynet are:

• A network of experts in the field of CPET
• A large crowdsourced data set
• An AI algorithm able to approximate human cognition in the analysis of CPET

We are interested in creating more research opportunities with other Universities and Departments, hospitals and clinics, medical doctors and physiologists (also operating in intensive care units), companies involved in the development (including patenting and validation) and in the commercialisation of medical devices (e.g. metabolic carts and medical software).

We want to bring together key actors from across sectors to jointly implement our R&D road map and: support the research activities financially (including scholarships for research fellows or publication fees for open access journals), provide intellectual contribution for scientific publications or grant application, share data for testing/developing new algorithms, develop web-based applications (e.g. crowdsourcing applications, automatic interpretation of new data, websites for communicating the outcomes of the project), conduct market and patent analyses, and validate the algorithms for clinical settings.

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Pip install the package

This is an example of how to list things you need to use the software and how to install them.

pip install pyoxynet

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Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

1. Get a free API Key at https://example.com
2. Clone the repo

   git clone https://github.com/your_username_/Project-Name.git
   

3. Install NPM packages

   npm install
   

4. Enter your API in config.js

   const API_KEY = 'ENTER YOUR API';
   

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Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the package Documentation

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Roadmap

• Create web app for inference
• Create web app for data crowd sourcing
• Create website
• Create Python package for inference
• ----
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  • ----
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See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Andrea Zignoli - @andrea_zignoli - [e-mail]

Repository project link: pyoxynet

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Acknowledgments

Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!

• Choose an Open Source License
• GitHub Emoji Cheat Sheet
• Malven's Flexbox Cheatsheet
• Malven's Grid Cheatsheet
• Img Shields
• GitHub Pages
• Font Awesome
• React Icons

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