ArticleJournal of pharmacokinetics and pharmacodynamics2019
Estimating parameters of nonlinear dynamic systems in pharmacology using chaos synchronization and grid search.
Article in Journal of pharmacokinetics and pharmacodynamics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Evaluation and analysis of clinical outcome prediction for trauma patients based on machine learning.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2026Article
- Recent applications of chaos theory in medical sciences: progress and challenges.BMC biomedical engineering · 2026Article
- Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Bridging fundamental approaches to model optimization for pharmacometricians, systems pharmacologists and statisticians is a critical issue. These fields rely primarily on Maximum Likelihood and Extended Least Squares metrics with iterative estimation of parameters. Our research combines adaptive chaos synchronization and grid search to estimate physiological and pharmacological systems with emergent properties by exploring deterministic methods that are more appropriate and have potentially superior performance than classical numerical approaches, which minimize the sum of squares or maximize the likelihood. We illustrate these issues with an established model of cortisol in human with nonlinear dynamics. The model describes cortisol kinetics over time, including its chaotic oscillations, by a delay differential equation. We demonstrate that chaos synchronization helps to avoid the tendency of the gradient-based optimization algorithms to end up in a local minimum. The subsequent analysis illustrates that the hybrid adaptive chaos synchronization for estimation of linear parameters with coarse-to-fine grid search for optimal values of non-linear parameters can be applied iteratively to accurately estimate parameters and effectively track trajectories for a wide class of noisy chaotic systems.
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Registered trials
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