Warfarin maintenance dose Prediction for Patients undergoing heart valve replacement— a hybrid model with genetic algorithm and Back-Propagation neural network

Li, Qian; Tao, Hongbing; Wang, Jing; Zhou, Qin; Chen, Jie; Wen, Qin; Dong, Li; Fu, Bo; Hou, Jiang Long; Chen, Jin; Zhang, Wei Hong

doi:10.1038/s41598-018-27772-9

Cited by 18 publications

(15 citation statements)

References 41 publications

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“…To date, no study has been speci cally designed to address this concern. Our previous studies found an extremely low prediction accuracy in the low-dose group (0.0% by BPNN [11] and 9.1% by ANFIS [12]) and high-dose group (0.0% by BPGA [10]). Considering the distribution of patients across different doses in the training set, the proportion in the medium-dose group was higher than that in the low-and high-dose groups (low-dose: 10.41%, medium-dose: 81.81%, high-dose: 7.78%).…”

Section: Reasons For Improved Prediction Property In Low-and High-dose Groupsmentioning

confidence: 85%

“…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]. In this study, we further included 15,108 patients who underwent HVR from 35 centers and used balanced training set preprocessing with the equal random strati ed sampling method.…”

Section: Summary Of Modelsmentioning

confidence: 99%

“…Thus, developing the optimal prediction model for warfarin dosing based on explicable clinical variables poses a challenging task. In previous studies, we established the Arti cial Neural Network (ANN) [9], the Back-Propagation neural network with Genetic Algorithm (BP-GA) [10], the Back Propagation Neural Network (BPNN) [11], and the Adapted Neural-Fuzzy Inference System (ANFIS) models [12], based on machine learning algorithms, to predict the maintenance dose of warfarin. As a result, we achieved the actual warfarin dose with 59-78% accuracy for patients who underwent AVR.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Reasons For Improved Prediction Property In Low-and High-dose Groupsmentioning

confidence: 85%

Section: Summary Of Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…It has been reported that a complex non‐linear relationship may exist between stable warfarin dose and genetic and clinical factors 19 . Therefore, a machine learning approach has been extensively implemented in developing predictive algorithms of personalized precision warfarin dosing 14,20–22 …”

Section: Introductionmentioning

confidence: 99%

Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population

Nguyen

Cho

et al. 2021

Journal of Thrombosis and Haemostasis

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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.

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“…In clinical pharmacology, GAs have been explored in the context of pharmacokinetic/pharmacodynamic (PK/PD) model selection, 2,3 the optimization of sampling times for PK studies, 4 and as alternative structural models to the multicompartment mammillary models in a machine learning approach to PK/PD 5 …”

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confidence: 99%

Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults

Colin

Eleveld

Thomson

2020

CPT Pharmacom & Syst Pharma

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This paper demonstrates the use of a genetic algorithm (GA) for the optimization of a dosing guideline. GAs are well-suited to derive combinations of doses and dosing intervals that go into a dosing guideline when the number of possible combinations rule out the calculation of all possible outcomes. GAs also allow for different constraints to be imposed on the optimization process to safeguard the clinical feasibility of the dosing guideline. In this work, we demonstrate the use of a GA for the optimization of intermittent vancomycin administration in adult patients. Constraints were placed on the dose strengths, the length of the dosing intervals, and the maximum infusion rate. In addition, flexibility with respect to the timing of the first maintenance dose was included in the optimization process. The GA-based optimal solution is compared with the Scottish Antimicrobial Prescribing Group vancomycin guideline. www.psp-journal.comGenetic Algorithm-Based Dosing Optimization Colin et al. www.psp-journal.com Genetic Algorithm-Based Dosing Optimization Colin et al.

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Warfarin maintenance dose Prediction for Patients undergoing heart valve replacement— a hybrid model with genetic algorithm and Back-Propagation neural network

Cited by 18 publications

References 41 publications

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

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

Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population

Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults

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