A toolkit for reproducible research in warfarin dose estimation
Project description
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.
Contents
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
Copyright (C) 2019 Gianluca Truda
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/.
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