AIMC Topic: Drug Monitoring
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Comparison of Machine Learning Algorithms and Bayesian Estimation in Predicting Tacrolimus Concentration in Tunisian Kidney Transplant Patients During the Early Post-Transplant Period.
European journal of drug metabolism and pharmacokinetics
May 8, 2025
BACKGROUND AND OBJECTIVE: Model-informed precision dosing (MIPD), based on a Bayesian approach and machine learning (ML) algorithms, is a suitable approach to personalize dosage recommendations and to improve the concentration target attainment for e...
Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study.
JMIR formative research
Apr 10, 2025
BACKGROUND: Unlike one-snap data collection methods that only identify high-risk patients, machine learning models using time-series data can predict adverse events and aid in the timely management of cancer.
Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study.
Journal of medical Internet research
Mar 31, 2025
BACKGROUND: Vancomycin is commonly dosed using standard weight-based methods before dose adjustments are made through therapeutic drug monitoring (TDM). However, variability in initial dosing can lead to suboptimal therapeutic outcomes. A predictive ...
Deep Learning-Assisted SERS for Therapeutic Drug Monitoring of Clozapine in Serum on Plasmonic Metasurfaces.
Nano letters
Mar 20, 2025
Clozapine is widely regarded as one of the most effective therapeutics for treatment-resistant schizophrenia. Despite its proven efficacy, the therapeutic use of clozapine is complicated by its narrow therapeutic index, which necessitates rapid and p...
Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation.
Journal of medical Internet research
Feb 19, 2025
BACKGROUND: Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to potential side effects. Robotic process automation uses software robots to automate repetitive and routine tasks, and their use may be exte...
Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.
Pharmaceutical research
Jan 22, 2025
OBJECTIVE: This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chinese adult epilepsy patients.
Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP).
BMJ open
Dec 12, 2024
INTRODUCTION: In sepsis treatment, achieving and maintaining effective antibiotic therapy is crucial. However, optimal antibiotic dosing faces challenges due to significant variability among patients with sepsis. Therapeutic drug monitoring (TDM), th...
Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape.
Drugs
Sep 6, 2024
Successful antimicrobial therapy depends on achieving optimal drug concentrations within individual patients. Inter-patient variability in pharmacokinetics (PK) and differences in pathogen susceptibility (reflected in the minimum inhibitory concentra...
Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.
Antimicrobial agents and chemotherapy
Aug 28, 2024
Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (...
Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.
Cell reports. Medicine
Aug 9, 2024
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingl...
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