Warfarin is an effective preventative treatment for arterial and venous thromboembolism, but requires individualised dosing due to its narrow therapeutic range and high individual variation. Many statistical and machine learning techniques have been demonstrated in this domain. This study evaluated the accuracy of the most promising algorithms on the International Warfarin Pharmacogenetics Consortium dataset and a novel clinical dataset of South African patients. Support vectors and linear regression were consistently amongst the top performers in both datasets and performed comparably to recent ensemble approaches . We also evaluated the use of genetic programming to design and optimise models without human guidance. Remarkably, these were found to match the performance of the best models hand-crafted by human experts. Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain  .
Warfarin is an effective preventative treatment for arterial and venous thromboembolism, but requires individualised dosing due to its narrow therapeutic range and high individual variation. Many machine learning techniques have been demonstrated in this domain. This study evaluated the accuracy of the most promising algorithms on the International Warfarin Pharmacogenetics Consortium dataset and a novel clinical dataset of South African patients. Support vectors and linear regression were amongst the top performers in both datasets and performed comparably to recent stacked ensemble approaches, whilst neural networks were one of the worst performers in both datasets . We also introduced genetic programming to automatically optimise model architectures and hyperparameters without human guidance. Remarkably, the generated models were found to match the performance of the best models hand-crafted by human experts. Finally, we present a novel software framework (Warfit-learn) for warfarin dosing research. It leverages the most successful techniques in preprocessing, imputation, and parallel evaluation, with the goal of accelerating research and making results in this domain more reproducible .
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statistical and
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consistently
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ensemble approaches
stacked ensemble approaches, whilst neural networks were one of the worst performers in both datasets
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evaluated the use of
introduced
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design and optimise models
automatically optimise model architectures and hyperparameters
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these
the generated models
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standardising future researchby leveraging
warfarin dosing research. It leverages
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evaluation--with
parallel evaluation, with
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making results more reproducible
accelerating research and making results
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more reproducible