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.

华法林是一种有效的预防动脉和静脉血栓栓塞的方法，但由于其治疗范围狭窄和个体差异较大，因此需要个体化剂量。在这一领域已经证明了许多机器学习技术。这项研究评估了国际华法林药物遗传学协会数据集和南非患者新型临床数据集上最有希望的算法的准确性。支持向量和线性回归在两个数据集中均表现最佳，与最近的集成集成方法相比表现最佳，而神经网络在两个数据集中均表现最差。我们还引入了遗传程序设计，无需人工指导即可自动优化模型架构和超参数。值得注意的是 发现生成的模型与人类专家手工制作的最佳模型的性能相匹配。最后，我们提出了一种用于华法林剂量研究的新颖软件框架（Warfit-learn）。它利用预处理，插补和并行评估中最成功的技术，以加速研究并使该领域的结果更具可重复性。