ArticleCPT: pharmacometrics & systems pharmacology2022
Application of SHAP values for inferring the optimal functional form of covariates in pharmacokinetic modeling.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.CPT: pharmacometrics & systems pharmacology · 2025Pooled it
- Digital Technologies in Hereditary Coagulation Disorders: A Systematic Review.Hamostaseologie · 2024Pooled it
- The effect of F8 missense variants on desmopressin response in people with nonsevere hemophilia A investigated using machine learning.Research and practice in thrombosis and haemostasis · 2026Article
- Stochastic Gates for Covariate Selection in Population Pharmacokinetics Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
- Explainable machine learning for early detection ofFrontiers in cellular and infection microbiology · 2026Article
- Building Hybrid Pharmacometric-Machine Learning Models in Oncology Drug Development: Current State and Recommendations.CPT: pharmacometrics & systems pharmacology · 2026Review
- Gut microbiome functional pathways outperform taxonomic profiles in predicting immune checkpoint inhibitor response in non-small cell lung cancer: an interpretable machine learning approach with SHAP.Frontiers in immunology · 2026Article
- Development and validation of a real-time risk prediction model for acute kidney injury in hospitalized pediatric patients.World journal of pediatrics : WJP · 2025Article
- Shap-Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling.CPT: pharmacometrics & systems pharmacology · 2025Article
- Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization.International journal of molecular sciences · 2025Review
- Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries.Scientific reports · 2025Article
- The development and validation of a prediction model for post-AKI outcomes of pediatric inpatients.Clinical kidney journal · 2025Article
- Machine learning-based prediction model for teicoplanin plasma concentrations in adults with liver disease using real-world data.Frontiers in pharmacology · 2025Article
- Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.Clinical and translational science · 2024Review
- Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.Clinical and translational science · 2024Article
- Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.Clinical and translational science · 2024Article
- On inductive biases for the robust and interpretable prediction of drug concentrations using deep compartment models.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Hematoma expansion prediction based on SMOTE and XGBoost algorithm.BMC medical informatics and decision making · 2024Article
- Learning pharmacometric covariate model structures with symbolic regression networks.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Low-dimensional neural ODEs and their application in pharmacokinetics.Journal of pharmacokinetics and pharmacodynamics · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In population pharmacokinetic (PK) models, interindividual variability is explained by implementation of covariates in the model. The widely used forward stepwise selection method is sensitive to bias, which may lead to an incorrect inclusion of covariates. Alternatives, such as the full fixed effects model, reduce this bias but are dependent on the chosen implementation of each covariate. As the correct functional forms are unknown, this may still lead to an inaccurate selection of covariates. Machine learning (ML) techniques can potentially be used to learn the optimal functional forms for implementing covariates directly from data. A recent study suggested that using ML resulted in an improved selection of influential covariates. However, how do we select the appropriate functional form for including these covariates? In this work, we use SHapley Additive exPlanations (SHAP) to infer the relationship between covariates and PK parameters from ML models. As a case-study, we use data from 119 patients with hemophilia A receiving clotting factor VIII concentrate peri-operatively. We fit both a random forest and a XGBoost model to predict empirical Bayes estimated clearance and central volume from a base nonlinear mixed effects model. Next, we show that SHAP reveals covariate relationships which match previous findings. In addition, we can reveal subtle effects arising from combinations of covariates difficult to obtain using other methods of covariate analysis. We conclude that the proposed method can be used to extend ML-based covariate selection, and holds potential as a complete full model alternative to classical covariate analyses.
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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.