ArticleMedical & biological engineering & computing2012
Characterisation of the iterative integral parameter identification method.
Article in Medical & biological engineering & computing, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A novel hierarchal-based approach to measure insulin sensitivity and secretion in at-risk populations.Journal of diabetes science and technology · 2014Trial
- Determining Losses in Jet Injection Subcutaneous Insulin Delivery: A Model-Based Approach.Journal of diabetes science and technology · 2023Article
- Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance.Journal of diabetes science and technology · 2022Article
- The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity.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
- Variability in Estimated Modelled Insulin Secretion.Journal of diabetes science and technology · 2022Article
- Stochastic integrated model-based protocol for volume-controlled ventilation setting.Biomedical engineering online · 2022Article
- Assessing the Asynchrony Event Based on the Ventilation Mode for Mechanically Ventilated Patients in ICU.Bioengineering (Basel, Switzerland) · 2021Article
- 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
- Insulin sensitivity in critically ill patients: are women more insulin resistant?Annals of intensive care · 2021Article
- Increased insulin resistance in intensive care: longitudinal retrospective analysis of glycaemic control patients in a New Zealand ICU.Therapeutic advances in endocrinology and metabolism · 2021Article
- Risk and reward: extending stochastic glycaemic control intervals to reduce workload.Biomedical engineering online · 2020Article
- 3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation.Biomedical engineering online · 2019Article
- 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
- Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices.Medical & biological engineering & computing · 2015Article
- Short-term glucagon stimulation test of C-peptide effect on glucose utilization in patients with type 1 diabetes mellitus.Medical & biological engineering & computing · 2015Article
- Use of the DISST model to estimate the HOMA and Matsuda indexes using only a basal insulin assay.Journal of diabetes science and technology · 2014Article
- Continuous glucose monitoring in newborn infants: how do errors in calibration measurements affect detected hypoglycemia?Journal of diabetes science and technology · 2014Article
- Iterative integral parameter identification of a respiratory mechanics model.Biomedical engineering online · 2012Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parameter identification methods are used to find optimal parameter values to fit models to measured data. The single integral method was defined as a simple and robust parameter identification method. However, the method did not necessarily converge to optimum parameter values. Thus, the iterative integral method (IIM) was developed. IIM will be compared to a proprietary nonlinear-least-squares-based Levenberg-Marquardt parameter identification algorithm using a range of reasonable starting values. Performance is assessed by the rate and accuracy of convergence for an exemplar two parameters insulin pharmacokinetic model, where true values are known a priori. IIM successfully converged to within 1% of the true values in all cases with a median time of 1.23 s (IQR 0.82-1.55 s; range 0.61-3.91 s). The nonlinear-least-squares method failed to converge in 22% of the cases and had a median (successful) convergence time of 3.29 s (IQR 2.04-4.89 s; range 0.42-44.9 s). IIM is a stable and relatively quick parameter identification method that can be applied in a broad variety of model configurations. In contrast to most established methods, IIM is not susceptible to local minima and is thus, starting point and operator independent.
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