ArticleCPT: pharmacometrics & systems pharmacology2025
Shap-Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Stochastic Gates for Covariate Selection in Population Pharmacokinetics Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Covariate identification in population pharmacokinetic/pharmacodynamic (popPK/PD) modeling is a key component in model development that is often prone to bias, time-consuming, and even intractable when too many covariates or complicated models are being considered. Early work leveraging machine learning (ML) for covariate screening has shown promising results over traditional methods. In this work, we expand this effort by integrating explainable machine learning facilitated by Shapley Additive Explanations (SHAP) analysis and covariate uncertainty quantification as well as a formal framework for establishing statistical significance of covariate relationships. Finally, we have packaged the proposed methodology into a flexible set of functions (shap-cov) to support popPK/PD modeling covariate identification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.