ArticleCPT: pharmacometrics & systems pharmacology2022
Deep compartment models: A deep learning approach for the reliable prediction of time-series data 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 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Digital Technologies in Hereditary Coagulation Disorders: A Systematic Review.Hamostaseologie · 2024Pooled it
- Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.CPT: pharmacometrics & systems pharmacology · 2026Article
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- Redefining Parameter Estimation and Covariate Selection via Variational Autoencoders: One Run Is All You Need.CPT: pharmacometrics & systems pharmacology · 2025Article
- QSP-Copilot: An AI-Augmented Platform for Accelerating Quantitative Systems Pharmacology Model Development.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
- Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.CPT: pharmacometrics & systems pharmacology · 2025Article
- Low-dimensional neural ordinary differential equations accounting for inter-individual variability implemented in Monolix and NONMEM.CPT: pharmacometrics & systems pharmacology · 2025Article
- Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.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
- Deep-NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data.CPT: pharmacometrics & systems pharmacology · 2024Article
- Low-dimensional neural ODEs and their application in pharmacokinetics.Journal of pharmacokinetics and pharmacodynamics · 2024Article
- Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Towards adequate and automated antibiotic dosing.Intensive care medicine · 2023Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.CPT: pharmacometrics & systems pharmacology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
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-learning methods, but most implementations cannot be reliably extrapolated to treatment strategies outside of the training data. In order to solve this problem, we propose the deep compartment model (DCM), a combination of neural networks and ordinary differential equations. Using simulated datasets of different sizes, we show that our model remains accurate when training on small data sets. Furthermore, using a real-world data set of patients with hemophilia A receiving factor VIII concentrate while undergoing surgery, we show that our model more accurately predicts a priori drug concentrations compared to a previous NLME model. In addition, we show that our model correctly describes the changing drug concentration over time. By adopting pharmacokinetic principles, the DCM allows for simulation of different treatment strategies and enables therapeutic drug monitoring.
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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.