期刊
JOURNAL OF THROMBOSIS AND HAEMOSTASIS
卷 19, 期 7, 页码 1676-1686出版社
WILEY
DOI: 10.1111/jth.15318
关键词
anticoagulants; linear models; supervised machine learning; thrombosis; warfarin
资金
- Ministry of Health & Welfare, Republic of Korea [HI15C1537]
- National Research Foundation of Korea [2018R1A5A2021242]
- National Research Foundation of Korea [2018R1A5A2021242] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This study compared multiple linear regression and machine learning algorithms for predicting personalized warfarin dosing in a Korean population. Both models showed similar performance in terms of accuracy, correlation, mean absolute error, and root mean square error. Genotypes, age, and weight were identified as the highest contributors to predicting the ideal warfarin dose.
Background Personalized warfarin dosing is influenced by various factors including genetic and non-genetic factors. Multiple linear regression (LR) is known as a conventional method to develop predictive models. Recently, machine learning approaches have been extensively implemented for warfarin dosing due to the hypothesis of non-linear association between covariates and stable warfarin dose. Objective To extend the multiple linear regression algorithm for personalized warfarin dosing in a Korean population and compare with a machine learning--based algorithm. Method From this cohort study, we collected information on 650 patients taking warfarin who achieved steady state including demographic information, indications, comorbidities, comedications, habits, and genetic factors. The dataset was randomly split into training set (90%) and test set (10%). The LR and machine learning (gradient boosting machine [GBM]) models were developed on the training set and were evaluated on the test set. Result LR and GBM models were comparable in terms of accuracy of ideal dose (75.38% and 73.85%), correlation (0.77 and 0.73), mean absolute error (0.58 mg/day and 0.64 mg/day), and root mean square error (0.82 mg/day and 0.9 mg/day), respectively. VKORC1 genotype, CYP2C9 genotype, age, and weight were the highest contributors and could obtain 80% of maximum performance in both models. Conclusion This study shows that our LR and GMB models are satisfactory to predict warfarin dose in our dataset. Both models showed similar performance and feature contribution characteristics. LR may be the appropriate model due to its simplicity and interpretability.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据