Using the Adapted Levenberg-Marquardt method to determine the validity of ignoring insulin and glucose data that is affected by mixing (2020)
Most parameter ID methods use least squares criterion to fit parameter values to observed behavior. However, the least squares criterion can be heavily influenced by outlying data or un-modelled effects. In such cases, least squares estimation can yield poor results. Outlying data is often manually removed to avoid inaccurate outcomes, but this process is complex, tedious and operator dependent.
This research presents an adaptation of the Levenberg-Marquardt (L-M) parameter identification method that effectively ignores least-square contributions from outlying data. The adapted method (aL-M) is capable of ignoring outlier data in accordance with the coefficient of variation of the residuals and was thus, capable of operator independent omission of outlier data using the 3 standard deviation rule. The aLM was compared to the original Levenberg-Marquardt (L-M) method in C-peptide, insulin and glucose data. In total three cases were tested: L-M in the full dataset, L-M in the same data where the points that were suspected to be affected by incomplete mixing at the depot site were removed, and the aL-M in the full data set.
There were strong correlations between the aL-M and the reduced dataset from [0.85, 0.71] for the clinically valuable glucose parameters. In contrast, the unreduced data yielded poor residuals and poor correlations with the aL-M [0.44, 0.33]. The aL-M approach provided strong justification for consistent removal of data that was deemed to be affected by mixing.
CitationLam N, Docherty PD, Murray R, Chase JG, Morenga LT (2020). Using the Adapted Levenberg-Marquardt method to determine the validity of ignoring insulin and glucose data that is affected by mixing. IFAC-PapersOnLine. 53(2). 16341-16346.
This citation is automatically generated and may be unreliable. Use as a guide only.
Keywordsglycemic modelling; least square estimation; outlier data; numerical optimisation
ANZSRC Fields of Research40 - Engineering::4003 - Biomedical engineering::400306 - Computational physiology
32 - Biomedical and clinical sciences::3202 - Clinical sciences::320208 - Endocrinology
49 - Mathematical sciences::4905 - Statistics::490502 - Biostatistics
RightsAll rights reserved unless otherwise stated
Showing items related by title, author, creator and subject.
Clinical application scenarios to handle insulin resistance and high endogenous glucose production for intensive care patients Yahia A; Benyo B; Chase, Geoff (Elsevier BV, 2020)Intensive care patients often experience hyperglycemia, insulin resistance (low insulin sensitivity), and high endogenous glucose production due to their critical situation. STAR is a model-based glycemic control protocol ...
Uyttendaele V; Gottlieb R; Shaw, Geoff; Desaive T; Knopp, Jennifer; Chase, Geoff (Elsevier BV, 2020)Glycaemic control (GC) has been associated with improved outcomes in critically ill patients. However, inter- and intra- patient metabolic variability significantly increase the risk of hypoglycaemia when using insulin ...
Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care Benyó B; Paláncz B; Szlávecz Á; Szabó B; Anane Y; Kovács K; Chase, Geoff (Elsevier BV, 2020)Stress-induced hyperglycaemia is a frequent complication in the intensive therapy that can be safely and efficiently treated by using the recently developed model-based tight glycaemic control (TGC) protocols. The most ...