Warfit-learn

A machine learning toolkit for reproducible research in warfarin dose estimation.

Read the paper on arXiv for free or on Science Direct with your institutional access.

Features
Installation
Usage
Citing this work

Features

Seamless loading, cleaning, and preprocessing of the IWPC warfarin dataset.
Standardised implementations of scoring functions.
Percentage patients within 20% of therapeutic dose (PW20)
Mean absolute error (MAE)
R² coefficient
Hybrid scoring functions
Confidence intervals
Multithreaded model evaluation using standardised resampling techniques.
Monte-carlo cross validation
Bootstrap resampling
Full interoperability with NumPy, SciPy, Pandas, Scikit-learn, and MLxtend.

Supports Python 3.6+ on macOS, Linux, and Windows.

Installation

pip install warfit-learn

Usage

For a detailed tutorial, see the Getting Started document.

Seamless loading and preprocessing of IWPC dataset

from warfit_learn import datasets, preprocessing
raw_iwpc = datasets.load_iwpc()
data = preprocessing.prepare_iwpc(raw_iwpc)

Full scikit-learn interoperability

from sklearn.linear_model import LinearRegression
from sklearn.svm import LinearSVR
from warfit_learn.estimators import Estimator
my_models = [
    Estimator(LinearRegression(), 'LR'),
    Estimator(LinearSVR(loss='epsilon_insensitive'), 'SVR'),
]

Seamless, multithreaded research

from warfit_learn.evaluation import evaluate_estimators
results = evaluate_estimators(
    my_models,
    data,
    parallelism=0.5,
    resamples=10,
)

Citing this work

If you use warfit-learn in a scientific publication, please consider citing the following paper:

G. Truda and P. Marais, Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation, Journal of Biomedical Informatics (2020), doi: https://doi.org/10.1016/j.jbi.2020.103634

BibTeX entry:

@article{Truda2020warfit,
  title = "Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation",
  journal = "Journal of Biomedical Informatics",
  pages = "103634",
  year = "2020",
  issn = "1532-0464",
  doi = "https://doi.org/10.1016/j.jbi.2020.103634",
  url = "http://www.sciencedirect.com/science/article/pii/S1532046420302628",
  author = "Gianluca Truda and Patrick Marais",
  keywords = "Warfarin, Machine learning, Genetic programming, Python, Supervised learning, Anticoagulant, Pharmacogenetics, Software",
}

Copyright

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

gianlucatruda/warfit-learn