ea98d211754cf73036a7988a07af4c61 BioMedInf_a2023v3n4p926.pdf 24f5a152a9b6dd3296b22da661e5c9bebe2f1c8c BioMedInf_a2023v3n4p926.pdf f679ac72a8baa512c932f8df2d4184c06e0b420eb4c59cebf2e45cc85da78c2d BioMedInf_a2023v3n4p926.pdf Title: Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients Subject: 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. Keywords: machine learning; pharmacokinetics; therapeutic drug monitoring; modeling; personalized medicine Author: Sergio Sánchez-Herrero, Laura Calvet and Angel A. Juan Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Mon Oct 16 08:28:51 2023 CEST ModDate: Mon Oct 16 08:34:40 2023 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 22 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 4693203 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MMYIQL+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 BTWXBG+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 VKHMVG+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 MNXRCP+CMSY10 Type 1 Builtin yes yes no 23 0 MNPEHI+CMEX10 Type 1 Builtin yes yes no 87 0 CZKGAS+CMR10 Type 1 Builtin yes yes no 91 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 95 0 UHIGNE+URWPalladioL-BoldItal Type 1 Custom yes yes no 102 0 [none] Type 3 Custom yes no no 112 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-04-24 02:45:57 CEST RepresentationInformation: BioMedInf_a2023v3n4p926.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-04-23 17:04:03 CEST Size: 4693203 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 421 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Data Destination: subsection.2.1 Item: Title: Validation Methods Destination: subsection.2.2 Item: Title: Models Destination: subsection.2.3 Item: Title: Performance Metrics Destination: subsection.2.4 Item: Title: External Evaluation Destination: subsection.2.5 Item: Title: Software Destination: subsection.2.6 Item: Title: Results Destination: section.3 Children: Item: Title: Basic Patient Characteristics Destination: subsection.3.1 Item: Title: Model Performance Destination: subsection.3.2 Item: Title: Feature Analysis Destination: subsection.3.3 Item: Title: Predictions of Tacrolimus Plasma Concentration over Time Destination: subsection.3.4 Item: Title: External Validation Destination: subsection.3.5 Item: Title: Clinical Significance Destination: subsection.3.6 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: section.6 Info: Title: Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients Author: Sergio Sánchez-Herrero, Laura Calvet and Angel A. Juan Subject: 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. 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