A physiologically based digital twin for alcohol consumption – predicting real-life drinking responses and long-term plasma PEth

This repository contains the code for the eHealth application prototype developed in the publication titled "A physiologically based digital twin for alcohol consumption – predicting real-life drinking responses and long-term plasma PEth", published in npj Digital Medicine.

The prototype is hosted at https://alcohol.streamlit.app, but can also be run locally. To do that, install the required packages listed in the requirements.txt file:

pip install -r requirements.txt

Then run the application by running streamlit run Home.py in the terminal.

Please note that the application can take a few minutes to start up, primarily when running at https://alcohol.streamlit.app, but also locally. Also note that a valid C-compiler is necessary for running the application locally.

The app was tested with Python 3.12.3, with the dependencies listed in requirements_full.txt. The requirements.txt file contains a slimmed version of the dependencies to avoid future dependency issues.

How to cite

If you use this application, please cite as:

Podéus H, Simonsson C, Nasr P, Ekstedt M, Kechagias S, Lundberg P, Lövfors W, Cedersund G (2024) A physiologically-based digital twin for alcohol consumption — predicting real-life drinking responses and long-term plasma PEth. npj Digital Medicine 7:112. https://doi.org/10.1038/s41746-024-01089-6

Or use the following bibtex entry:

@article{podeus_2024,
	title = {A physiologically-based digital twin for alcohol consumption — predicting real-life drinking responses and long-term plasma {PEth}},
	volume = {7},
	issn = {2398-6352},
	url = {https://doi.org/10.1038/s41746-024-01089-6},
	doi = {10.1038/s41746-024-01089-6},
	number = {1},
	journal = {npj Digital Medicine},
	author = {Podéus, Henrik and Simonsson, Christian and Nasr, Patrik and Ekstedt, Mattias and Kechagias, Stergios and Lundberg, Peter and Lövfors, William and Cedersund, Gunnar},
	month = may,
	year = {2024},
	pages = {112},
}