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Journal article
Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose
PloS one, v 13(10), pp e0205872-e0205872
19 Oct 2018
PMID: 30339708
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose
- Creators
- Zhiyuan Ma - Duke UniversityPing Wang - Easton Cardiovascular Associates, Easton, PA, United States of America.Zehui Gao - San Diego State UniversityRuobing Wang - Department of Chemistry and Social Science Research Institute, Duke University, Durham, NC, United States of AmericaKoroush Khalighi - Drexel University
- Publication Details
- PloS one, v 13(10), pp e0205872-e0205872
- Publisher
- Public Library Science
- Number of pages
- 12
- Grant note
- Science Center of Opera Solutions LLC
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Medicine (Graduate)
- Web of Science ID
- WOS:000447761400022
- Scopus ID
- 2-s2.0-85055077844
- Other Identifier
- 991019168523604721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Pharmacology & Pharmacy