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Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose

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  • Zhiyuan Ma
  • Ping Wang
  • Zehui Gao
  • Ruobing Wang
  • Koroush Khalighi

Abstract

Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The most widely validated pharmacogenetic algorithm is the IWPC algorithm based on multivariate linear regression (MLR). However, with only a single algorithm, the prediction performance may reach an upper limit even with optimal parameters. Here, we present novel algorithms using stacked generalization frameworks to estimate the warfarin dose, within which different types of machine learning algorithms function together through a meta-machine learning model to maximize the prediction accuracy. Compared to the IWPC-derived MLR algorithm, Stack 1 and 2 based on stacked generalization frameworks performed significantly better overall. Subgroup analysis revealed that the mean of the percentage of patients whose predicted dose of warfarin within 20% of the actual stable therapeutic dose (mean percentage within 20%) for Stack 1 was improved by 12.7% (from 42.47% to 47.86%) in Asians and by 13.5% (from 22.08% to 25.05%) in the low-dose group compared to that for MLR, respectively. These data suggest that our algorithms would especially benefit patients requiring low warfarin maintenance dose, as subtle changes in warfarin dose could lead to adverse clinical events (thrombosis or bleeding) in patients with low dose. Our study offers novel pharmacogenetic algorithms for clinical trials and practice.

Suggested Citation

  • Zhiyuan Ma & Ping Wang & Zehui Gao & Ruobing Wang & Koroush Khalighi, 2018. "Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-12, October.
  • Handle: RePEc:plo:pone00:0205872
    DOI: 10.1371/journal.pone.0205872
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    References listed on IDEAS

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    1. Rong Liu & Xi Li & Wei Zhang & Hong-Hao Zhou, 2015. "Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-11, August.
    2. Simone Rost & Andreas Fregin & Vytautas Ivaskevicius & Ernst Conzelmann & Konstanze Hörtnagel & Hans-Joachim Pelz & Knut Lappegard & Erhard Seifried & Inge Scharrer & Edward G. D. Tuddenham & Clemens , 2004. "Mutations in VKORC1 cause warfarin resistance and multiple coagulation factor deficiency type 2," Nature, Nature, vol. 427(6974), pages 537-541, February.
    3. Kim, Jaehoon & Kim, Sangsin, 2015. "2012년 국회법 개정의 효과 연구 [A Study on the Effect of the 2012 National Assembly Act Amendment]," KDI Research Monographs, Korea Development Institute (KDI), volume 127, number v:2015-03(k):y:2015:p:1-1.
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    1. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    2. Jordan J Bird & Chloe M Barnes & Cristiano Premebida & Anikó Ekárt & Diego R Faria, 2020. "Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.

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