[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123153-en":3,"doc-seo-123153-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123153,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients","Tacrolimus has a narrow therapeutic index and substantial toxicity and interindividual variability, requiring frequent therapeutic drug monitoring and individualized dose adjustments in pediatric renal transplant recipients. This study compares multiple machine learning models that use pharmacokinetic data to predict tacrolimus blood concentration, enabling safer dose decisions. Data were split into derivation and validation cohorts, and performances were assessed across several algorithms. ExtraTreesRegressor achieved the best accuracy metrics and supported regulatory-acceptable prediction quality despite limited sample size through resampling.","Article  \nMachine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients  \nSergio Sánchez-Herrero 1, Laura Calvet 2 and Angel A. Juan 3, *  \nCitation: Sánchez-Herrero, S.; Calvet, L.; Juan, A.A. Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients. BioMedInformatics 2023, 3, 926–947 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)biomedinformatics3040057  \nAcademic Editors: José Machado and Alexandre G. De Brevern  \nReceived: 11 August 2023  \nRevised: 8 October 2023  \nAccepted: 12 October 2023  \nPublished: 14 October 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya, 08018 Barcelona, Spain  \n2 Telecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona, Carrer Emprius, 2, 08202 Sabadell, Spain  \n3 Research Center on Production Management and Engineering, Universitat Politècnica de València, Plaza Ferrandiz-Salvador, 03801 Alcoy, Spain  \n* Correspondence: ajuanp@upv.es  \nAbstract: Tacrolimus, characterized by a narrow therapeutic index, significant toxicity, adverse effects, and interindividual variability, necessitates frequent therapeutic drug monitoring and dose adjustments in renal transplant recipients. This study aimed to compare machine learning (ML) models utilizing pharmacokinetic data to predict tacrolimus blood concentration. This prediction underpins crucial dose adjustments, emphasizing patient safety. The investigation focuses on a pediatric cohort. A subset served as the derivation cohort, creating the dose-prediction algorithm, while the remaining data formed the validation cohort. The study employed various ML models, including artificial neural network, RandomForestRegressor, LGBMRegressor, XGBRegressor, AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor, KNeighborsRegressor, and support vector regression, and their performances were compared. Although all models yielded favorable fit outcomes, the ExtraTreesRegressor (ETR) exhibited superior performance. It achieved measures of 􀀀0.161 for MPE, 0.995 for AFE, 1.063 for AAFE, and 0.8 for R2, indicating accurate predictions and meeting regulatory standards. The findings underscore ML's predictive potential, despite the limited number of samples available. To address this issue, resampling was utilized, offering a viable solution within medical datasets for developing this pioneering study to predict tacrolimus trough concentration in pediatric transplant recipients.  \nKeywords: machine learning; pharmacokinetics; therapeutic drug monitoring; modeling; personalized medicine  \n1. Introduction  \nTraditionally, pharmacokinetic (PK) parameters inhuman therapeutic drug monitoring (TDM) have been estimated using in vitro and in vivo methods. Pharmacokinetic data are frequently utilized in pharmacokinetic/pharmacodynamic (PKPD) studies to establish the relationship between drug exposure and response, such as the area under the concentration– time curve (AUC) . However, when sparse data methods are employed, population PK/PD models (popPKPD) are suitable and commonly employed for understanding the exposure– response relationship [1,2] .  \nMachine learning methods have emerged as powerful tools in pharmacokinetics methodology, marking a new trend. They enable the management of intricate relationships within large datasets and the analysis of high-dimensional data in clinical practice. The recent integration of artificial intelligence (AI) has further propelled the utilization of ML f","cbCairZas3S5mDtt","https://ap.wps.com/l/cbCairZas3S5mDtt","pdf",4693203,1,22,"English","en",105,"# Introduction\n## Machine Learning in Pharmacokinetics\n## Sparse Data and Population PK/PD Models\n## Related Work on ML-Based PK Prediction","[{\"question\":\"Why is predicting tacrolimus stable dosage important in pediatric renal transplant patients?\",\"answer\":\"Tacrolimus has a narrow therapeutic index, notable toxicity, and high variability between individuals, so frequent therapeutic drug monitoring and dose adjustment are needed to support patient safety.\"},{\"question\":\"How was the dataset organized for the machine learning dose-prediction study?\",\"answer\":\"A derivation cohort was used to build the dose-prediction algorithm, while the remaining data formed a validation cohort for performance comparison across models.\"},{\"question\":\"Which machine learning model performed best and what does that indicate?\",\"answer\":\"ExtraTreesRegressor showed the superior performance among tested models, producing accurate concentration predictions and meeting regulatory standards, indicating strong predictive potential for tacrolimus trough concentration.\"}]","Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients | 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is predicting tacrolimus stable dosage important in pediatric renal transplant patients?","Question",{"text":75,"@type":76},"Tacrolimus has a narrow therapeutic index, notable toxicity, and high variability between individuals, so frequent therapeutic drug monitoring and dose adjustment are needed to support patient safety.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset organized for the machine learning dose-prediction study?",{"text":80,"@type":76},"A derivation cohort was used to build the dose-prediction algorithm, while the remaining data formed a validation cohort for performance comparison across models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what does that indicate?",{"text":84,"@type":76},"ExtraTreesRegressor showed the superior performance among tested models, producing accurate concentration predictions and meeting regulatory standards, indicating strong predictive potential for tacrolimus 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