Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese

Tao, Yanyun; Chen, Yenming J.; Fu, Xiangyu; Bin, Jiang; Zhang, Yuzhen

doi:10.1109/jbhi.2018.2812165

Cited by 25 publications

(8 citation statements)

References 30 publications

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“…Therefore, in terms of the coefficient of determination ( R 2 ), R 2 for the IWPC algorithm in Asians is 0.46 [ 26 ], which is not inferior to the “bagging” models (Ms+G) ranging from 0.402 to 0.441 for Chinese in the previous study [ 13 ]. “Bagging” method requires designing various feature functions for base models in order to achieve diversity, which is a key requirement for base models [ 13 ]. The novel regression models we proposed here can take advantage of distinct machine learning algorithms to achieve high diversity of base models.…”

Section: Discussionmentioning

confidence: 98%

“…In addition, the ensemble method “bagging” has been used to predict warfarin dose [ 12 , 13 ], which is a popular method to assemble base models to decrease the variance, but not to improve the predictive force. Therefore, in terms of the coefficient of determination ( R 2 ), R 2 for the IWPC algorithm in Asians is 0.46 [ 26 ], which is not inferior to the “bagging” models (Ms+G) ranging from 0.402 to 0.441 for Chinese in the previous study [ 13 ]. “Bagging” method requires designing various feature functions for base models in order to achieve diversity, which is a key requirement for base models [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…ensemble method). Recently, the ensemble method “bagging” has been applied to predict warfarin dose [ 12 , 13 ]. Stacked generalization is another ensemble method that uses a higher-level model to combine lower-level models to achieve higher prediction accuracy [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

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

Wang²,

Gao

et al. 2018

PLoS ONE

102

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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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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

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

Wang²,

Gao

et al. 2018

PLoS ONE

102

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“…Several works have demonstrated the possibility that algorithms basing on machine learning can predict warfarin dose requirements more precisely 18 , 23 – 25 . However, given that their study numbers are relatively small, the patients might not represent all warfarin users 15 , 24 , 25 . These works also usually focus on the overall predicting performance, with limited further discussion on subgroups.…”

Section: Discussionmentioning

confidence: 99%

Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling

Dong

et al. 2021

Sci Rep

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Patients requiring low-dose warfarin are more likely to suffer bleeding due to overdose. The goal of this work is to improve the feedforward neural network model's precision in predicting the low maintenance dose for Chinese in the aspect of training data construction. We built the model from a resampled dataset created by equal stratified sampling (maintaining the same sample number in three dose-groups with a total of 3639) and performed internal and external validations. Comparing to the model trained from the raw dataset of 19,060 eligible cases, we improved the low-dose group's ideal prediction percentage from 0.7 to 9.6% and maintained the overall performance (76.4% vs. 75.6%) in external validation. We further built neural network models on single-dose subsets to invest whether the subsets samples were sufficient and whether the selected factors were appropriate. The training set sizes were 1340 and 1478 for the low and high dose subsets; the corresponding ideal prediction percentages were 70.2% and 75.1%. The training set size for the intermediate dose varied and was 1553, 6214, and 12,429; the corresponding ideal prediction percentages were 95.6, 95.1%, and 95.3%. Our conclusion is that equal stratified sampling can be a considerable alternative approach in training data construction to build drug dosing models in the clinic.

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“…The MLR method presents certain irreconcilable issues such as poorly behavior of the non-linear relationship between variables; thus, the MLR is unlikely to be an optimal method for predicting the warfarin dose [33]. Recently, several arti cial intelligence modeling technologies, including support vector machines and a general regression neural network, have been used for warfarin dosage predication [34,35]; however, these models showed a relatively low predictive ability of < 50% in the ideal predicted percentage. Our study team has made numerous attempts in the eld of warfarin model development and achieved a 63% predictive accuracy based on BPGA and ANFIS models [10,36,12].…”

Section: Summary Of Modelsmentioning

confidence: 99%

An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement

Huang

Dong

et al. 2021

Cardiovasc Drugs Ther

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BackgroundTailoring warfarin use poses a challenge for physicians and pharmacists due to its narrow therapeutic window and huge inter-individual variability. This study aimed to create an adapted neural-fuzzy inference system (ANFIS) model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in Chinese patients undergoing heart valve replacement (HVR). MethodsThis retrospective study enrolled patients who underwent HVR between June 1, 2012 and June 1, 2016 from 35 centers in China. The primary outcomes were the mean difference between predicted warfarin dose by ANFIS models and actual dose, and the models' predictive accuracy, including the ideal predicted percentage, the mean absolute error (MAE), and the mean squared error (MSE). The eligible cases were divided into training, internal validation, and external validation groups. We explored input variables by univariate analysis of a general liner model and created two ANFIS models using imbalanced and balanced training sets. We nally compared the primary outcomes between the imbalanced and balanced ANFIS models in both internal and external validation sets. Strati ed analyses were conducted across warfarin doses (low, medium, and high doses). ResultsA total of 15,108 patients were included and grouped as follows: 12,086 in the imbalanced training set; 2,820 in the balanced training set; 1,511 in the internal validation set; and 1,511 in the external validation set. Eight variables were explored as predictors related to warfarin maintenance doses, and imbalanced and balanced ANFIS models with multi-fuzzy rules were developed. The results showed a low mean difference between predicted and actual doses (< 0.3 mg/d for each model) and an accurate prediction property in both the imbalanced model (ideal prediction percentage: 74.39-78.16%, MAE: 0.37 mg/daily, MSE: 0.39 mg/daily) and the balanced model (ideal prediction percentage: 73.46-75.31%, MAE: 0.42 mg/daily; MSE, 0.43 mg/daily). Compared to the imbalanced model, the balanced model had a signi cantly higher prediction accuracy in the low-dose (14.46% vs. 3.01%; P < 0.001) and the high-dose warfarin groups (34.71% vs. 23.14%; P = 0.047). The results from the external validation cohort con rmed this nding. ConclusionsThe ANFIS model can accurately predict the warfarin maintenance dose in patients after HVR. Through data preprocessing, the balanced model contributed to improved prediction ability in the low-and high-

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Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese

Cited by 25 publications

References 30 publications

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

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

Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling

An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement

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