ArticleBiomedical engineering online2011
A graphical method for practical and informative identifiability analyses of physiological models: a case study of insulin kinetics and sensitivity.
Article in Biomedical engineering online, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart.Journal of pharmacokinetics and pharmacodynamics · 2017Trial
- Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.Bulletin of mathematical biology · 2024Article
- Determining Losses in Jet Injection Subcutaneous Insulin Delivery: A Model-Based Approach.Journal of diabetes science and technology · 2023Article
- The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity.Journal of diabetes science and technology · 2022Article
- Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance.Journal of diabetes science and technology · 2022Article
- The Impact of Exogenous Insulin Input on Calculating Hepatic Clearance Parameters.Journal of diabetes science and technology · 2022Article
- Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients.Annals of biomedical engineering · 2021Observational
- Estimating Increased EGP During Stress Response in Critically Ill Patients.Journal of diabetes science and technology · 2021Article
- Capacity of Infusion Lines for Insulin Adsorption: Effect of Flow Rate on Total Adsorption.Journal of diabetes science and technology · 2021Article
- Measuring lung mechanics of expiratory tidal breathing with non-invasive breath occlusion.Biomedical engineering online · 2020Article
- Optimising mechanical ventilation through model-based methods and automation.Annual reviews in control · 2019Review
- Model-based glycemic control in a Malaysian intensive care unit: performance and safety study.Medical devices (Auckland, N.Z.) · 2019Article
- Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems.Journal of diabetes science and technology · 2018Article
- Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them.Biomedical engineering online · 2018Review
- Article
- The necessity of identifying the basal glucose set-point in the IVGTT for patients with Type 2 Diabetes.Biomedical engineering online · 2015Article
- Article
- Characterisation of the iterative integral parameter identification method.Medical & biological engineering & computing · 2012Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDerivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error.
methodsThe method measures the distinction of the integral formulations of the parameter coefficients at the proposed sampling times. It can thus predict the susceptibility of the parameters to the effects of measurement error. The method is tested in-silico with Monte Carlo analyses of a number of insulin sensitivity test applications.
resultsThe method successfully captured the analogous nature of identifiability observed in Monte Carlo analyses of a number of cases including protocol alterations, parameter changes and differences in participant behaviour. However, due to the numerical nature of the analyses, prediction was not perfect in all cases.
conclusionsThus although the current method has valuable and significant capabilities in terms of study or test protocol design, additional developments would further strengthen the predictive capability of the method. Finally, the method captures the experimental reality that sampling error and timing can negate assumed parameter identifiability and that identifiability is a continuous rather than discrete phenomenon.
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