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Code repository for the paper "Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations".

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SI-AIEP-paper

Running python code

The python code was developed using poetry as a package manager. After installing poetry the repository can be initialized by running:

$ poetry install

The virtual environment containing the installed packages and the python binary is included in the repository to make it easier to use this repository. This does not require you to actually have poetry installed. The python binary can be accessed by running:

$ .venv/bin/python

The model code can be run from inside this REPL. Alternatively a file can be directly run by calling:

.venv/bin/python $file

where $file for example equals plot_concentration_predictions.py

Installing pyearth

Since the pyearth package is not available to install directly from pip (it might be from conda if you have it), we have to jump through some hoops to install it. Please follow the installation instructions here.

If you are not getting it to work, I've had many problems with it as well. In order to still be able to run the code, please comment the lines relating to the evaluation of the pyearth model in plot_covariate_selection.py.

Running julia code

Make sure you have julia installed, and then run:

$ julia --project=.
julia> ]
(SI-AIEP-paper) pkg> instantiate

Now you can either run the julia code from the REPL or call:

$ julia $file --project=. 

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Code repository for the paper "Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations".

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