matt-graham / patientflow

Code and training materials for predicting short-term hospital bed demand using real-time data

PatientFlow

Code and training materials for predicting short-term hospital bed capacity using real-time data

This project is developed in collaboration with the Centre for Advanced Research Computing, University College London.

About

Project Team

Zella King ([e-mail])

Research Software Engineering Contact

Centre for Advanced Research Computing, University College London ([e-mail])

Built With

• Framework 1
• Framework 2
• Framework 3

Getting Started

Prerequisites

patientflow requires Python 3.10–3.12.

Installation

We recommend installing in a project specific virtual environment created using a environment management tool such as Conda. To install the latest development version of patientflow using pip in the currently active environment run

pip install git+https://github.com/zmek/patientflow.git

Alternatively create a local clone of the repository with

git clone https://github.com/zmek/patientflow.git

and then install in editable mode by running

pip install -e .

Running Locally

How to run the application on your local system.

Running Tests

Tests can be run across all compatible Python versions in isolated environments using tox by running

tox

To run tests manually in a Python environment with pytest installed run

pytest tests

again from the root of the repository.

Building Documentation

The MkDocs HTML documentation can be built locally by running

tox -e docs

from the root of the repository. The built documentation will be written to site.

Alternatively to build and preview the documentation locally, in a Python environment with the optional docs dependencies installed, run

mkdocs serve

Roadmap

• Initial Research
• Minimum viable product <-- You are Here
• Alpha Release
• Feature-Complete Release

Acknowledgements

This work was funded by a grant from the UCL Impact Funding.