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Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation.

Abstract：

: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.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Truda G,Marais P

doi

10.1016/j.jbi.2020.103634

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

103634

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(20)30262-8

journal_volume

113

pub_type

杂志文章

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