Reducing the Effect of Outlying Data on the Identification of Insulinaemic Pharmacokinetic Parameters with an Adapted Gauss-Newton Approach

Docherty, Paul; Gray, Rebecca; Mansell, Erin J.

doi:10.3182/20140824-6-za-1003.01351

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Assuming the residuals are normally distributed, use of the modified GN method should have no negative effects when outlier behaviour does not exist in the data, and the result in this case should closely match the outcome of the original GN method (Docherty et al, 2014). Thus, the method could safely be used to provide clinicians with patient-specific information in situations where the patient is not spontaneously breathing, as well as when SB is present.…”

Section: Discussionmentioning

confidence: 99%

“…5 indicates that β = 4 was optimal in this analysis for effectively ignoring M-waves. Assuming the residual error is normally distributed, the value of β equals the number of standard deviations (SD) of the error distribution that is between the peaks of the objective contribution shape (Docherty et al, 2014). For example, when β = 2, the largest contribution to the step in x happens for residuals that are two SDs from the mean.…”

Section: Discussionmentioning

confidence: 99%

“…A β value of infinity means that the original GN method is applied as I is multiplied by one. However, if β is small, the approach will ignore important characteristics that define the system (Docherty et al, 2014).…”

Section: Modified Gauss-newtonmentioning

confidence: 99%

“…In this paper, the NARX model is applied to M-wave data in conjunction with a modified version of the Gauss-Newton (GN) parameter identification algorithm. The modified GN method has previously been used to ignore contributions from outlying data by finding the parameter set that fits the majority of the data points, rather than the least squares optima for all data points (Docherty et al, 2014). The aim was to use these two approaches to model respiratory mechanics while effectively ignoring M-waves in the pressure signal.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

et al. 2015

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Modelling the respiratory system of intensive care patients can enable individualized mechanical ventilation therapy and reduce ventilator induced lung injuries. However, spontaneous breathing (SB) efforts result in asynchronous pressure waveforms that mask underlying respiratory mechanics. In this study, a nonlinear auto-regressive (NARX) model was identified using a modified Gauss-Newton (GN) approach, and demonstrated on data from one SB patient. The NARX model uses three pressure dependent basis functions to capture respiratory system elastance, and contains a single resistance coefficient and positive end expiratory pressure (PEEP) coefficient. The modified GN method exponentially reduces the contribution of large residuals on the step in the coefficients at each GN iteration. This approach allows the model to effectively ignore the anomaly in the pressure waveform due to SB efforts, while successfully describing the shape of normal breathing cycles. This method has the potential to be used in the ICU to more robustly capture patient-specific behaviour, and thus enable clinicians to select optimal ventilator settings and improve patient care.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Modified Gauss-newtonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

et al. 2015

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“…2,15 Another method adapted the Gauss-Newton gradient-descent parameter identification method to limit the influence of outliers, 12 where subsequent comparison of identified insulin sensitivity estimates to a standard modelling approach showed it effectively captured model parameters typically obscured by unmodelled mixing dynamics. 16,17 A third approach is to include an additional local mixing compartment in the model. Caumo et al 18 showed increasing model compartments could lead to improved parameter identification.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity

Lam

Murray

Docherty

et al. 2021

J Diabetes Sci Technol

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Background: The identification of insulin sensitivity in glycemic modelling can be heavily obstructed by the presence of outlying data or unmodelled effects. The effect of data indicative of local mixing is especially problematic with models assuming rapid mixing of compartments. Methods such as manual removal of data and outlier detection methods have been used to improve parameter ID in these cases, but modelling data with more compartments is another potential approach. Methods: This research compares a mixing model with local depot site compartments with an existing, clinically validated insulin sensitivity test model. The Levenberg-Marquardt (LM) parameter identification method was implemented alongside a modified version (aLM) capable of operator-independent omission of outlier data in accordance with the 3 standard deviation rule. Three cases were tested: LM where data points suspected to be affected by incomplete mixing at the depot site were removed, aLM, and LM with the more complex mixing model. Results: While insulin parameters identified in the mixing model differed greatly from those in the DISST model, there were strong Spearman correlations of approximately 0.93 for the insulin sensitivity values identified across all 3 methods. The 2 models also showed comparable identification stability in insulin sensitivity estimation through a Monte Carlo analysis. However, the mixing model required modifications to the identification process to improve convergence, and still failed to converge to feasible parameters on 5 of the 212 trials. Conclusions: The mixing compartment model effectively captured the dynamics of mixing behavior, but with no significant improvement in insulin sensitivity identification.

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Optimization of identifying insulinaemic pharmacokinetic parameters using artificial neural network

Othman

Azhar

Damanhuri

et al. 2023

Computer Methods and Programs in Biomedicine

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Reducing the Effect of Outlying Data on the Identification of Insulinaemic Pharmacokinetic Parameters with an Adapted Gauss-Newton Approach

Cited by 6 publications

References 9 publications

Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity

Optimization of identifying insulinaemic pharmacokinetic parameters using artificial neural network

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