Objective The proper handling of missing values is critical to delivering reliable estimates
and decisions, especially in high-stakes fields such as clinical research. In response to the …

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates
pharmacokinetic and pharmacodynamic data from many subjects to understand inter-and …

Recently, the use of machine‐learning (ML) models for pharmacokinetic (PK) modeling has
grown significantly. Although most of the current approaches use ML techniques as black …

Abstract Machine Learning (ML) is a fast-evolving field, integrated in many of today's
scientific disciplines. With the recent development of neural ordinary differential equations …

Background Host and environment early-life risk factors are associated with progression of
wheezing symptoms over time; however, their individual contribution is relatively small. We …

This study uses machine learning techniques to explore the relationships between
contemporary sexist attitudes and demographic and socioeconomic factors. A total of 1110 …

Electronic health records (EHRs) provide meaningful knowledge of drug‐related adverse
events (AEs) that are not captured in standard drug development and postmarketing …

Modeling the relationships between covariates and pharmacometric model parameters is a
central feature of pharmacometric analyses. The information obtained from covariate …

ABSTRACT A growing number of covariate modeling methods have been proposed in the
field of popPK modeling, but limited information exists on how they all compare. The …

BACKGROUND AND AIMS: Pharmacometric in silico approaches are frequently applied to
guide decisions concerning dosage regimes during the development of new medicines. We …