An Application of Classification Analysis for Skewed Class Distribution in Therapeutic Drug Monitoring - The Case of Vancomycin

Chen, Jianxun; Cheng, Tsang-Hsiang; Chan, Allen; Wang, Hue-Yu

doi:10.1109/ideadh.2004.6

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…Machine learning (ML) has emerged as a promising tool in TDM to predict vancomycin e cacy and optimize dosage [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Its ability to process large, high-dimensional datasets makes it well-suited for applications in patient groups with signi cant heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Optimizing vancomycin dosing in pediatrics: A machine learning approach to predict trough concentrations in children under four years of age

Yin,

Jiang,

Yuan

et al. 2024

Preprint

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Background Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant interindividual variability and rapid physiological changes during maturation. Aim This study aimed to develop a machine-learning (ML) model to predict vancomycin trough concentrations and determine optimal dosing regimens in pediatric patients using various machine-learning (ML) algorithms. Method A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring (TDM) were enrolled. Seven ML models [linear regression (LR), gradient boosted decision trees (GDBT), support vector machine (SVM), decision tree (DT), random forest (RF), Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked. Results The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration. Conclusion An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support decision-support technology.

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Section: Introductionmentioning

confidence: 99%

Optimizing vancomycin dosing in pediatrics: A machine learning approach to predict trough concentrations in children under four years of age

Yin,

Jiang,

Yuan

et al. 2024

Preprint

View full text Add to dashboard Cite

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Data driven forest fire susceptibility mapping in Bangladesh

Haydar,

Hossain Rafi,

Sadia

et al. 2024

Ecological Indicators

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Neural System for in silico Drug-Drug Interaction Screening

Polak

Brandys

Mendyk

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

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Drug usage is always associated with risk. Drugs interactions are considered to be one of the potential sources of undesirable action of drugs. Such a situation enforced U.S. Food and Drug Administration (FDA) as well as European Medicines Agency (EMEA) to issue a guidance for industry and researchers for in vivo and in vitro drug interactions studies. The authorsproposed neural networks based in silico system for potential drug-drug interactions screening with use of simple physico-chemical data describing each chemical substance particle. Initial results where 77% of classification rate in generalization test was found suggest that computational intelligence based systems could be effective in this area.

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An Application of Classification Analysis for Skewed Class Distribution in Therapeutic Drug Monitoring - The Case of Vancomycin

Cited by 9 publications

References 16 publications

Optimizing vancomycin dosing in pediatrics: A machine learning approach to predict trough concentrations in children under four years of age

Optimizing vancomycin dosing in pediatrics: A machine learning approach to predict trough concentrations in children under four years of age

Data driven forest fire susceptibility mapping in Bangladesh

Neural System for in silico Drug-Drug Interaction Screening

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