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Disease course prediction using random forest + conformal prediction trained on SMSREG data

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(Multiple Sclerosis Progression-tracker)


Setting up the environment

To create and activate the environment.

conda env create -f environment.yml
conda activate websmsreg

To export the conda environment to Jupyter Notebook.

python -m ipykernel install --user --name=websmsreg


Data preprocessing and creating data splits

The data can be collected from Swedish MS REGistry (SMSREG). The data shall be kept in a designated folder. The path to the data can be provided in the notebook (data_cleaning_and_splitting.ipynb) under the input section.

Training

Code for training and evaluation of both the model and conformal prediction is given in notebook (random_forest_cp.ipynb)

The model and scripts used for the website model are available in the folder gradio.
To run the website locally, activate the conda environment and run

python3 gradio/app.py

Citation

Please cite:

Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis
Akshai Parakkal Sreenivasan, Aina Vaivade, Yassine Noui, Payam Emami Khoonsari, Joachim Burman, Ola Spjuth, Kim Kultima*
Status: Submitted.

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Disease course prediction using random forest + conformal prediction trained on SMSREG data

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