AI Medical Compendium Journal:
CPT: pharmacometrics & systems pharmacology

Showing 21 to 30 of 38 articles

An introduction to causal inference for pharmacometricians.

CPT: pharmacometrics & systems pharmacology
As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic f...

Evaluation of machine learning methods for covariate data imputation in pharmacometrics.

CPT: pharmacometrics & systems pharmacology
Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, suc...

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

CPT: pharmacometrics & systems pharmacology
The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well-established for evaluating exposure-response (E-R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evalua...

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.

CPT: pharmacometrics & systems pharmacology
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific dat...

Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

CPT: pharmacometrics & systems pharmacology
Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-lear...

Introduction of an artificial neural network-based method for concentration-time predictions.

CPT: pharmacometrics & systems pharmacology
Pharmacometrics and the application of population pharmacokinetic (PK) modeling play a crucial role in clinical pharmacology. These methods, which describe data with well-defined equations and estimate physiologically interpretable parameters, have n...

Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm.

CPT: pharmacometrics & systems pharmacology
Warfarin remains the most widely prescribed oral anticoagulant in sub-Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine-learning tec...

Heterogeneous treatment effect analysis based on machine-learning methodology.

CPT: pharmacometrics & systems pharmacology
Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE-informed understanding can critically guide physicians to individualize the medical treatme...