J. Chase

发表

Model-based glycaemic control: methodology and initial results from neonatal intensive care

Jennifer L. Dickson, J. Dickson, J. Chase, 2017, Biomedizinische Technik. Biomedical engineering.

Wavelet Signatures and Diagnostics for the Assessment of ICU Agitation-Sedation Protocols

J. Geoffrey Chase, Irene Hudson, In Kang, 2011 .

Evaluation of a plasma insulin model for glycaemic control in intensive care

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Impact of control on agitation–sedation dynamics

Geoffrey M. Shaw, Dominic S. Lee, Graeme C. Wake, 2005 .

Modeling and control of the agitation-sedation cycle for critical care patients.

Dominic S. Lee, Geoffrey M Shaw, Graeme C Wake, 2004, Medical engineering & physics.

Identification of patient specific parameters for a minimal cardiac model

C. Hann, J. Chase, G. Shaw, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Quantifying agitation in sedated ICU patients using heart rate and blood pressure.

Christina Starfinger, Geoffrey M Shaw, Franck Agogue, 2004, Physiological measurement.

Derivative weighted active insulin control algorithms and intensive care unit trials

Geoffrey M. Shaw, Katherine T. Moorhead, Carmen V. Doran, 2005 .

Derivative weighted active insulin control modelling and clinical trials for ICU patients.

Geoffrey M Shaw, C. Hann, J. Chase, 2004, Medical engineering & physics.

Derivative weighted active insulin control algorithms and trials

Geoffrey M. Shaw, Katherine T. Moorhead, Carmen V. Doran, 2003 .

A Subcutaneous Insulin Pharmacokinetic Model for Computer Simulation in a Diabetes Decision Support Role: Model Structure and Parameter Identification

Christopher E Hann, Jessica Lin, Geoffrey M Shaw, 2008, Journal of diabetes science and technology.

Endogenous insulin secretion and suppression during and after sepsis in critically ill patients: implications for tight glycemic control protocols

J. Chase, U. Jamaludin, C. Pretty, 2011, Critical Care.

Pre-ejection period, the reason why the electrocardiogram Q-wave is an unreliable indicator of pulse wave initialization

J. Chase, G. Shaw, C. Pretty, 2018, Physiological measurement.

Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas

R. Hovorka, J. Chase, E. Renard, 2018, Critical Care.

Simulation of Left Atrial Function Using a Multi-Scale Model of the Cardiovascular System

Antoine Pironet, Thomas Desaive, Pierre C. Dauby, 2013, PloS one.

In Silico Simulation of Long-Term Type 1 Diabetes Glycemic Control Treatment Outcomes

Christopher E. Hann, Geoffrey M. Shaw, Jessica Lin, 2008, Journal of diabetes science and technology.

Investigating the Effects of Temperature on Photoplethysmography

Geoffrey M. Shaw, Christopher G. Pretty, R. B. Elliott, 2015 .

Insulin Glargine in the intensive care unit: A model-based clinical trial design

J. Geoffrey Chase, Geoffrey M. Shaw, Normy N. Razak, 2013, Biomed. Signal Process. Control..

A pilot study of continuous glucose monitoring in critically ill patients: Do they perform well enough for use in glycaemic control?

J. Chase, G. Shaw, L. Fisk, 2014 .

Attributes, Performance, and Gaps in Current & Emerging Breast Cancer Screening Technologies.

J. Chase, C. Pretty, H. Ismail, 2019, Current medical imaging reviews.

Glucose control positively influences patient outcome: A retrospective study.

J. Chase, G. Shaw, C. Pretty, 2015, Journal of critical care.

Evaluation of a Glomerular Filtration Term in the DISST Model to Capture the Glucose Pharmacodynamics of an Insulin-Resistant Cohort

J. Chase, T. Lotz, P. Docherty, 2011 .

The dynamic insulin sensitivity and secretion test--a novel measure of insulin sensitivity.

J. Chase, G. Shaw, J. Mann, 2011, Metabolism: clinical and experimental.

A three-compartment model of the C-peptide-insulin dynamic during the DIST test.

S. Andreassen, C. Hann, J. Chase, 2010, Mathematical biosciences.

Accuracy of Stroke Volume Estimation via Reservoir Pressure Concept and Three Element Windkessel Model

J. Chase, G. Shaw, C. Pretty, 2014 .

The Glucosafe system for tight glycemic control in critical care: a pilot evaluation study.

B. S. Nielsen, S. Andreassen, J. Chase, 2010, Journal of critical care.

When to measure blood glucose - cohort-specific glycaemic control

S. Andreassen, J. Chase, U. Pielmeier, 2008 .

Increased insulin resistance in intensive care: longitudinal retrospective analysis of glycaemic control patients in a New Zealand ICU

J. Chase, G. Shaw, J. Knopp, 2021, Therapeutic advances in endocrinology and metabolism.

An integrated framework for damage diagnosis and prognosis in long-term structural health monitoring

C. Zhou, J. Chase, 2019 .

Risk and reward: extending stochastic glycaemic control intervals to reduce workload

J. Chase, G. Shaw, T. Desaive, 2020, BioMedical Engineering OnLine.

Analysis of Aortic Energetics from Pulse Wave Examination in a Porcine Study of Septic Shock

P. Kolh, J. Chase, G. Shaw, 2012 .

Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study

Jennifer L. Dickson, J. Dickson, J. Chase, 2018, Journal of diabetes science and technology.

Automation in Critical Care : Future Directions in Sedation Delivery

C. Hann, J. Chase, G. Shaw, 2006 .

Comparison of modulated and clinically set nutrition protocol’s for STAR

Jennifer L. Dickson, Anna R. Hardy, J. Dickson, 2018 .

The Effect of Variable vs Fixed Feeding on Glycaemic Control in the Adult ICU: Virtual Trial Evaluation

Kent W. Stewart, J. Chase, G. Shaw, 2017 .

Generalizability of a Nonlinear Model-based Glycemic Controller

Kent W. Stewart, J. Chase, G. Shaw, 2016 .

Survey about diffusion and adoption of glycaemic controller in European intensive care units

J. Chase, C. Pretty, T. Desaive, 2014 .

Determining the relative efficacy of a number of PID and PD models that relate insulin secretion to bolus induced glucose excursions

J. Chase, N. A. Othman, P. Docherty, 2014 .

Model-based Modified OGTT Insulin Sensitivity Test Design

Thomas Desaive, Paul D. Docherty, Jennifer L. Knopp, 2018 .

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model

J. Chase, G. Shaw, K. Möller, 2022, Biomedical engineering online.

Tight Glycaemic Control in Critical Care Using Insulin and Nutrition - the SPRINT Protocol

Christopher E. Hann, G. M. Shaw, Dominic S. Lee, 2006 .

Interpolation within a Recruitment Manoeuvre using a Non-Linear Autoregressive Model of Pulmonary Mechanics

J. Chase, K. Möller, Ruby Langdon, 2015 .

Modeled insulin sensitivity and interstitial insulin action from a pilot study of dynamic insulin sensitivity tests

Normy N. Razak, C. Hann, J. Chase, 2010 .

DISTq: Low-cost, accurate and real-time estimation of insulin sensitivity

J. Mann, C. Hann, J. Chase, 2009 .

A Novel Hierarchal-Based Approach to Measure Insulin Sensitivity and Secretion in At-Risk Populations

Liam M. Fisk, J. Chase, P. Docherty, 2014, Journal of diabetes science and technology.

Evaluation of the Performances and Costs of a Spectrum of DIST Protocols

J. Mann, C. Hann, J. Chase, 2010 .

Intensive Control Insulin-Nutrition-Glucose Model validated in critically ill patients

Normy N. Razak, C. Hann, J. Chase, 2010 .

Corticosteroids and insulin resistance in the ICU

J. Chase, G. Shaw, C. Pretty, 2009 .

Viscous Damping in Actuated Breast Tissue to Detect Tumors in a Digital Image Elasto Tomography (DIET) System

J. Chase, Cong Zhou, Marcus Haggers, 2018 .

Nominal Stiffness Identification for Tumor Detection of Women Breast in a Digital Image Elasto Tomography (DIET) System

J. Chase, C. Pretty, Cong Zhou, 2017 .

Assessment of ventricular-arterial coupling with a model-based sensor

P. Kolh, C. Hann, J. Chase, 2010 .

Estimating endogenous glucose production during exercise using heart rate: implications for diabetes management

J. Chase, J. Knopp, T. Zhou, 2021, IFAC-PapersOnLine.

Insulin sensitivity in critically ill patients: are women more insulin resistant?

J. Chase, G. Shaw, T. Desaive, 2021, Annals of Intensive Care.

Insulin Resistance in ICU Patients: Women Have Stronger Metabolic Response

J. Chase, G. Shaw, T. Desaive, 2020 .

Observation of incretin effects during enteral feed transitions of critically ill patients

J. Chase, G. Shaw, J. Preiser, 2012 .

Reducing the Length of Mechanical Ventilation with Significance: A Case Study of Sample Size Estimation Trial Design Using Monte-Carlo Simulation

E. Moltchanova, J. Chase, G. Shaw, 2015 .

Reducing the Length of echanical Ventilation with Significance : A Case Study of Sample Size Estimation Trial Design Using onte-Carlo Si ulation

E. Moltchanova, J. Chase, G. Shaw, 2015 .

Development of virtual patients for use in mechanical ventilation

J. Chase, G. Shaw, J. Knopp, 2019 .

Low cost circulatory pressure acquisition and fluid infusion rate measurement system for clinical research

Chris J. Cameron, J. Chase, G. Shaw, 2022, HardwareX.

Daily Evolution of Insulin Sensitivity Variability with Respect to Diagnosis in the Critically Ill

Liam M. Fisk, J. Chase, G. Shaw, 2013, PloS one.

The goldilocks problem: Nutrition and its impact on glycaemic control.

J. Chase, G. Shaw, J. Knopp, 2021, Clinical nutrition.

Is intensive insulin therapy the scapegoat for or cause of hypoglycaemia and poor outcome?

J. Chase, G. Shaw, T. Desaive, 2019, IFAC J. Syst. Control..

Benchtop to bedside to worldwide: Implementing model-based glycemic control in intensive care

J. Chase, G. Shaw, A. Compte, 2012 .

Insulin Kinetics during Hyper-Insulinemia Euglycemia Therapy (HIET)

J. Chase, T. Desaive, A. Compte, 2012 .

Continuous Glucose Monitoring: Using CGM to Guide Insulin Therapy Virtual Trials Results

J. Chase, F. Thomas, L. Mombaerts, 2015 .

Clinical Compliance in Personalised Model-based Medical Decision Support: Do computers and interfaces yield better compliance?

Jennifer L. Dickson, J. Dickson, J. Chase, 2019, IFAC-PapersOnLine.

mAGiC DRAGONS: A Protocol for Accurate Glycaemic Control in General Wards

Liam M. Fisk, J. Dickson, J. Chase, 2014 .

Pilot Study of the SPRINT Glycemic Control Protocol in a Hungarian Medical Intensive Care Unit

Liam M. Fisk, J. Chase, G. Shaw, 2012, Journal of diabetes science and technology.

Model-based Monitoring of Septic Shock Treated with Large-pore Hemofiltration Therapy

P. Kolh, J. Chase, G. Shaw, 2012 .

Basis Function Modelling of Respiratory Patients with High or Low Auto-PEEP

J. Chase, Ruby Langdon, P. Docherty, 2017 .

Analysis of Neonatal Pulmonary Mechanics

Kyeong Tae Kim, J. Dickson, J. Chase, 2017 .

Plateau Pressure Prediction in ARDS Patients

J. Chase, Ruby Langdon, P. Docherty, 2017 .

Model-Based Approach to Estimate Dfrc in the ICU Using Measured Lung Dynamics

J. Chase, G. Shaw, Y. Chiew, 2012 .

Estimation of End Expiratory Volume using Stress Strain

C. Hann, J. Chase, G. Shaw, 2009 .

Estimating patient-specific maximum recruitable volume in neonatal lungs

J. Chase, J. Knopp, Bronwyn Dixon, 2021, IFAC-PapersOnLine.

A Fast and Accurate Diagnostic Test for Severe Sepsis Using Kernel Classifiers

Dominic S. Lee, J. Chase, G. Shaw, 2010 .

Mechanical behaviour of tissue mimicking breast phantom materials

J. Chase, C. Pretty, Cong Zhou, 2017 .

Rayleigh Damping Modelling for Tumor Detection using Digital Image Elasto Tomography (DIET)

J. Chase, Cong Zhou, Marcus Haggers, 2020 .

A Surface Vibration-based Method for Tumor Detection of Women Breast in a DIET System

J. Chase, C. Pretty, Cong Zhou, 2017 .

STAR-3D Clinical Trial Results: Improved performance and safety

J. Chase, T. Desaive, J. Knopp, 2021, IFAC-PapersOnLine.

Translating A Risk-Based Glycaemic Control Framework for Critically Ill Patients: STAR-Liège

J. Chase, G. Shaw, B. Lambermont, 2020 .

Traversing the valley of glycemic control despair

J. Dickson, J. Chase, 2017, Critical Care.

The CREBRF diabetes-protective rs373863828-A allele is associated with enhanced early insulin release in men of Māori and Pacific ancestry

T. Merriman, J. Chase, P. Shepherd, 2021, Diabetologia.

Agitation Sensor Based on Facial Grimacing for Improved Sedation Management in Critical Care

X. Q. Chen, C. Hann, J. Chase, 2007 .

Structural identifiability analysis of a cardiovascular system model.

J. Chase, P. Dauby, T. Desaive, 2016, Medical engineering & physics.

Improving Sedation in Intensive Care: New drugs or better methods?

J. Chase, G. Shaw, 2009 .

Automated Agitation Management Accounting for Saturation Dynamics

J. Chase, G. Shaw, A. D. Rudge, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Physiologically-based minimal model of agitation-sedation dynamics

J. Chase, G. Shaw, A. D. Rudge, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Modelling and control of the agitation-sedation cycle

G. Wake, J. Chase, G. Shaw, 2003 .

Early clinical trial termination: Simulation-based design of a robust stopping rule using difference in interventional effect on mortality

Jennifer L. Dickson, J. Dickson, J. Chase, 2016, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

3D Stochastic Modelling of Insulin Sensitivity in STAR: Virtual trials analysis

J. Chase, G. Shaw, T. Desaive, 2018 .

Preliminary results from the STAR-Liège clinical trial: Virtual trials, safety, performance, and compliance analysis

J. Chase, G. Shaw, B. Lambermont, 2018 .

Improved 3D Stochastic Modelling of Insulin Sensitivity Variability for Improved Glycaemic Control

J. Chase, T. Desaive, J. Knopp, 2018 .

Virtual Trials of the NICE-SUGAR Protocol: The Impact on Performance of Protocol and Protocol Compliance

Jennifer L. Dickson, J. Dickson, J. Chase, 2017 .

Insulin sensitivity variability during hypothermia

J. Chase, G. Shaw, C. Pretty, 2014 .

On the Problem of Patient-Specific Endogenous Glucose Production in Neonates on Stochastic Targeted Glycemic Control

J. Dickson, J. Chase, A. Lynn, 2013, Journal of diabetes science and technology.

Concurrent Continuous Glucose Monitoring in Critically Ill Patients: Interim Results and Observations

Liam M. Fisk, J. Chase, G. Shaw, 2013, Journal of diabetes science and technology.

Commentary Is there more to glycaemic control than meets the eye

J. Chase, G. Shaw, 2007 .

An in-silico Study of Using Continuous Glucose Monitoring Measures for Glycaemic Control in the ICU.

J. Chase, G. Shaw, J. Knopp, 2018 .

Safe and effective glycaemic control in premature infants: observational clinical results from the computerised STAR-GRYPHON protocol

J. Chase, A. Lynn, G. Shaw, 2018, Archives of Disease in Childhood: Fetal and Neonatal Edition.

Development and Pilot Trial Results of Stochastic Targeted (STAR) Glycemic Control in a Medical ICU

Liam M. Fisk, J. Chase, G. Shaw, 2012 .

Variability of insulin sensitivity during the first 4 days of critical illness

J. Chase, J. Preiser, T. Desaive, 2012, Critical Care.

Improving Safety of Glucose Control in Intensive Care using Virtual Patients and Simulated Clinical Trials

Liam M. Fisk, J. Chase, G. Shaw, 2012 .

Glargine as a Basal Insulin Supplement in Recovering Critically Ill Patients - An In Silico Study

J. Chase, G. Shaw, C. Pretty, 2010 .

Tight Glycemic Control - The leading role of insulin sensitivity in determining efficacy and thus outcome

Normy N. Razak, C. Hann, J. Chase, 2009 .

Blood Glucose Control in Neonatal Intensive Care with Model-Based Controllers

C. Hann, J. Chase, A. Lynn, 2009 .

Clinical Validation of a Model-based Glycaemic Control Design Approach and Comparison to Other Clinical Protocols

C. Hann, J. Chase, G. Shaw, 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

INSULIN + NUTRITION CONTROL FOR TIGHT CRITICAL CARE GLYCAEMIC REGULATION

C. Hann, J. Chase, G. Shaw, 2006 .

An in-silico Analysis of the Ability of Dynamic Tests to Trace the Kinetic Behaviour of Insulin Sensitizer Drugs

J. Chase, G. Shaw, T. Lotz, 2011 .

A fully identifiable physiological model of insulin kinetics for clinical applications

S. Andreassen, C. Hann, J. Chase, 2005 .

Cost Analysis of Treating Neonatal Hypoglycemia with Dextrose Gel

Darrell M. Wilson, L. Doyle, J. Chase, 2018, The Journal of pediatrics.

Glycaemic State Analysis from Continuous Glucose Monitoring Measurements in Infants

J. Chase, G. Gamble, J. Harding, 2018 .

Continuous glucose monitoring in neonates: a review

Jennifer L. Dickson, J. Dickson, J. Chase, 2017, Maternal Health, Neonatology and Perinatology.

Pilot study of a model-based approach to blood glucose control in very-low-birthweight neonates

J. Chase, A. Lynn, G. Shaw, 2012, BMC Pediatrics.

Model-based estimation of Frank-Starling curves at the patient bedside

J. Chase, G. Shaw, C. Pretty, 2021, IFAC-PapersOnLine.

Modelling Insulin Clearance and Degradation in Very Premature Infants

Liam M. Fisk, J. Dickson, J. Chase, 2015 .

Variability of Model Based Insulin Sensitivity in Liver Transplanted Patients

J. Chase, B. Benyó, J. Homlok, 2014 .

Altered blood glucose dynamics during and after anhepatic phase of liver transplantation: A model-based approach

J. Chase, B. Benyó, P. Docherty, 2013, 2013 IEEE 17th International Conference on Intelligent Engineering Systems (INES).

Continuous glucose monitors and tight glycaemic control in intensive care: An in-silico proof of concept analysis

J. Chase, G. Shaw, C. Pretty, 2010 .

A Clinician's View of Engineering and Technology in Intensive Care - Model-based Therapeutics and Patient Outcomes

C. Hann, J. Chase, G. Shaw, 2010 .

Model-Based Glycaemic Control in Critically Ill Diabetes Mellitus Patients: Monte Carlo Sensitivity Analysis

A. A. Razak, J. Chase, N. Razak, 2022, 2022 12th International Conference on Biomedical Engineering and Technology (ICBET).

Diagnosis using a minimal cardiac model including reflex actions

Christopher E. Hann, G. M. Shaw, Steen Andreassen, 2005 .

Robust Parameter Identification for Cardiac diagnosis in critical care using minimal data sets

P. Kolh, C. Hann, J. Chase, 2011 .

Assessment of SOFA Score as a Diagnostic Indicator in Intensive Care Medicine

J. Chase, G. Shaw, C. Pretty, 2012 .

Clinical application scenarios to handle insulin resistance and high endogenous glucose production for intensive care patients

J. Chase, B. Benyó, Anane Yahia, 2020 .

Gender and glycaemia: Insulin sensitivity and secretion in premature neonates

J. Dickson, J. Chase, A. Lynn, 2014 .

Structural Identifiability Analysis of a Cardiovascular System Model

J. Geoffrey Chase, Antoine Pironet, Thomas Desaive, 2014 .

Model-based management of cardiovascular failure: Where medicine and control systems converge

J. Geoffrey Chase, Thomas Desaive, Jayme Pinto Ortiz, 2019, Annu. Rev. Control..

Physiological trend analysis of a novel cardio-pulmonary model during a preload reduction manoeuvre

J. Chase, G. Shaw, T. Desaive, 2022, Comput. Methods Programs Biomed..

Virtual Patient Modeling and Prediction Validation for Pressure Controlled Mechanical Ventilation

J. Chase, M. Tawhai, K. Moeller, 2020 .

Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data

Thomas Desaive, J Geoffrey Chase, Shaun Davidson, 2019, Comput. Methods Programs Biomed..

Humans are Horribly Variable

J. Dickson, J. Chase, C. Gunn, 2014 .

Performance and Safety of STAR Glycaemic Control in Neonatal Intensive Care: Further Clinical Results Including Pilot Results from a New Protocol Implementation

Liam M. Fisk, J. Dickson, J. Chase, 2014 .

STAR-Liège Clinical Trial Interim Results: Safe and Effective Glycemic Control for All

J. Geoffrey Chase, Geoffrey M. Shaw, Philippe Morimont, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Oral diazoxide versus placebo for severe or recurrent neonatal hypoglycaemia: Neonatal Glucose Care Optimisation (NeoGluCO) study – a randomised controlled trial

J. Chase, C. Mckinlay, J. Alsweiler, 2022, BMJ Open.

Quantifying neonatal patient effort using non-invasive model-based methods

Kyeong Tae Kim, J. Chase, J. Knopp, 2022, Medical & Biological Engineering & Computing.

Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance

J. Chase, G. Shaw, B. Benyó, 2021, Journal of diabetes science and technology.

Nasogastric Aspiration as an Indicator for Feed Absorption in Model-Based Glycemic Control in Neonatal Intensive Care

J. Dickson, J. Chase, A. Lynn, 2013, Journal of diabetes science and technology.

Prediction Validation of Two Glycaemic Control Models in Critical Care

Geoffrey M. Shaw, Steen Andreassen, Ulrike Pielmeier, 2008 .

Prediction of hemodynamic changes towards PEEP titrations at different volemic levels using a minimal cardiovascular model

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2008, Comput. Methods Programs Biomed..

Cardiovascular modelling and the Intensive Care Unit clinician

P. Kolh, J. Chase, G. Shaw, 2012 .

Improving model-based cardiac diagnosis with an ECG

P. Kolh, C. Hann, J. Chase, 2009 .

Patient-specific modelling of the cardiovascular system - application to septic shock with a minimal data set

P. Kolh, C. Hann, J. Chase, 2009 .

A simulation model of insulin saturation and glucose balance for glycemic control in ICU patients

Steen Andreassen, Ulrike Pielmeier, J. Geoffrey Chase, 2010, Comput. Methods Programs Biomed..

Performance of variations of the dynamic elastance model in lung mechanics

J. Chase, K. Möller, Ruby Langdon, 2017 .

Non-invasive over-distension measurements: data driven vs model-based

J. Chase, G. Shaw, K. Möller, 2022, Journal of clinical monitoring and computing.

Outcome Improvement Between the First Two Waves of the Coronavirus Disease 2019 Pandemic in a Single Tertiary-Care Hospital in Belgium

J. Chase, B. Lambermont, A. Rousseau, 2021, Critical care explorations.

Patient-Ventilator Synchrony and Tidal Volume Variability using NAVA and Pressure Support Mechanical Ventilation Modes

J. Chase, B. Lambermont, J. Roeseler, 2011 .

Basis function identification of lung mechanics in mechanical ventilation for predicting outcomes of therapy changes: A first virtual patient

J. Dickson, J. Chase, G. Shaw, 2018 .

A Variable Resistance Respiratory Mechanics Model

Kyeong Tae Kim, J. Chase, D. Redmond, 2017 .

Sensitivity Analysis for Stiffness Identification Using a DIET Breast Cancer Screening System

J. Chase, C. Pretty, Cong Zhou, 2017 .

Non-invasive measurement of tidal breathing lung mechanics using expiratory occlusion

J. Chase, C. Pretty, K. Möller, 2020 .

Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function

J. Chase, G. Shaw, K. Möller, 2019, Annals of Biomedical Engineering.

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

J. Chase, P. Docherty, L. T. Te Morenga, 2021, Journal of diabetes science and technology.

Iterative Interpolative Pressure Reconstruction for Improved Respiratory Mechanics Estimation During Asynchronous Volume Controlled Ventilation

J. Chase, G. Shaw, C. Pretty, 2015 .

Pressure reconstruction method for spontaneous breathing effort monitoring

J. Chase, N. Damanhuri, P. Docherty, 2015, Critical Care.

Hyperglycaemic Preterm Babies Have Sex Differences in Insulin Secretion

Jennifer L. Dickson, J. Dickson, J. Chase, 2015, Neonatology.

Velocity profile method for time varying resistance in minimal cardiovascular system models.

Bram W. Smith, J. Chase, G. Shaw, 2003, Physics in medicine and biology.

A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control

J. Geoffrey Chase, Geoffrey M. Shaw, Thomas Desaive, 2018, Biomed. Signal Process. Control..

Physiological Relevance of a Minimal Model in Healthy Pigs Lung

J. Chase, G. Shaw, B. Lambermont, 2012 .

Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics

J. Chase, Ruby Langdon, P. Docherty, 2017, Biomedical engineering online.

Modelling patient specific cardiopulmonary interactions

J. Chase, G. Shaw, T. Desaive, 2022, Comput. Biol. Medicine.

Monte Carlo Analysis of a Subcutaneous Absorption Insulin Glargine Model: Variability in Plasma Insulin Concentrations

Normy N. Razak, J. Chase, Jessica Lin, 2012 .

Quantifying patient effort in spontaneously breathing patient using negative component of dynamic Elastance

Kyeong Tae Kim, J. Chase, D. Redmond, 2017 .

Negative Lung Elastance in Mechanically Ventilated Spontaneously Breathing Patient

J. Chase, G. Shaw, T. Desaive, 2017 .

Time-Varying Respiratory Elastance for Spontaneously Breathing Patients

Sarah F. Poole, J. Chase, G. Shaw, 2014 .

The separation of insulin pump hardware and software - a novel and low-cost approach to insulin pump design

Jake D. Campbell, J. Chase, J. Knopp, 2021, IFAC-PapersOnLine.

Modelling the cardiovascular system.

Bram W. Smith, C. Hann, J. Chase, 2007, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

Virtual Patients for Managing Mechanical Ventilation in the ICU

J. Chase, J. Knopp, S. Morton, 2019 .

Validation of a Model-based Method for Estimating Functional Volume Gains during Recruitment Manoeuvres in Mechanical Ventilation

J. Chase, G. Shaw, P. Docherty, 2018 .

Predicting the Effects of Changing PEEP Using a Basis Function Method

Jennifer L. Dickson, Kyeong Tae Kim, J. Dickson, 2017 .

Glycemic control in the intensive care unit: A control systems perspective

J. Geoffrey Chase, Balázs Benyó, Thomas Desaive, 2019, Annu. Rev. Control..

Development of a Predictive Pulmonary Elastance Model to Describe Lung Mechanics throughout Recruitment Manoeuvres

J. Chase, G. Shaw, M. Tawhai, 2018 .

Preliminary Studies into Acoustic Sensing of Lung Recruitment During Mechanical Ventilation

J. Chase, G. Shaw, G. Rodgers, 2015 .

Identifying pressure dependent elastance in lung mechanics with reduced influence of unmodelled effects

J. Chase, K. Möller, P. Docherty, 2015 .

Specific compliance: is it truly independent of lung volume?

J. Dickson, J. Chase, O. Kannangara, 2018 .

Model-based patient matching for in-parallel pressure-controlled ventilation

J. Chase, T. Desaive, Y. Chiew, 2022, Biomedical engineering online.

Model-based Patient Matching for in-parallel Multiplexing Mechanical Ventilation Support

J. Chase, T. Desaive, Y. Chiew, 2021, IFAC-PapersOnLine.

Managing Patient-Specific Mechanical Ventilation: Clinical Utilisation of Respiratory Elastance (CURE) – Model and Software Development

Sarah F. Poole, J. Chase, G. Shaw, 2014 .

Breath Ammonia Reduction Ratio (ARR) Measures Dialysis Efficacy

Dominic S. Lee, J. Chase, Z. Endre, 2007 .

Letter to the Editor in response to “COVID-19: desperate times call for desperate measures”

J. Chase, G. Shaw, B. Lambermont, 2020, Critical Care.

Unique parameter identification of a cardiovascular system model using feedback control

C. Hann, J. Chase, G. Shaw, 2009, 2009 IEEE International Conference on Control and Automation.

Model-based therapeutics for the Cardiovascular System - a Clinical Focus

P. Kolh, C. Hann, J. Chase, 2009 .

Model-based sensor of hemodynamics in critical care

C. Hann, J. Chase, G. Shaw, 2007 .

Breathing Easier: Model-based decision support for respiratory care looks beyond tomorrow.

J. Chase, M. Tawhai, 2015, IEEE Pulse.

Computationally efficient velocity profile solutions for cardiac haemodynamics

C. Hann, J. Chase, G. Shaw, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

ESICM 2012 MONDAY SESSIONS 15 October, 2012

A. Verstraete, J. Chase, G. Shaw, 2012, Intensive Care Medicine.

CPAP pressure and flow data at 2 positive pressure levels and multiple controlled breathing rates from a trial of 30 adults

J. Chase, J. Knopp, Lui Holder-Pearson, 2022, BMC research notes.

Physiologic-range flow and pressure sensor for respiratory systems

J. Chase, Lui Holder-Pearson, 2021, HardwareX.

High Inter-Patient Variability in Sepsis Evolution: A Hidden Markov Model Analysis

Geoffrey M. Shaw, Jacquelyn D. Parente, Knut Möller, 2021, Comput. Methods Programs Biomed..

Hidden Markov Models for Sepsis Classification

J. Geoffrey Chase, Geoffrey M. Shaw, Knut Möller, 2018 .

Biomedical engineer’s guide to the clinical aspects of intensive care mechanical ventilation

J. Geoffrey Chase, Geoffrey M. Shaw, Yeong Shiong Chiew, 2018, BioMedical Engineering OnLine.

A virtual patient model for mechanical ventilation

J. Geoffrey Chase, Geoffrey M. Shaw, Thomas Desaive, 2018, Comput. Methods Programs Biomed..

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

P. Hunter, J. Dickson, J. Chase, 2018, Biomedical engineering online.

Point: HOMA—Satisfactory for the Time Being

Geoffrey M Shaw, J. Chase, G. Shaw, 2007, Diabetes Care.

Impact of insulin-stimulated glucose removal saturation on dynamic modelling and control of hyperglycaemia

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2005, Int. J. Intell. Syst. Technol. Appl..

Simulation of cardiovascular system diseases by including the autonomic nervous system into a minimal model

J. Geoffrey Chase, Geoffrey M. Shaw, Steen Andreassen, 2007, Comput. Methods Programs Biomed..

Cardiovascular system modelling of heart-lung interaction during mechanical ventilation

Bram W. Smith, S. Andreassen, J. Chase, 2006 .

Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model

J. Chase, N. Damanhuri, N. A. Othman, 2022, Comput. Methods Programs Biomed..

Inspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles

J G Chase, S L Howe, D P Redmond, 2019, Comput. Methods Programs Biomed..

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

J. Geoffrey Chase, Yeong Shiong Chiew, Vincent Major, 2016, Comput. Methods Programs Biomed..

Feasibility of titrating PEEP to minimum elastance for mechanically ventilated patients

Yeong Shiong Chiew, Thomas Desaive, Bernard Lambermont, 2015, Pilot and Feasibility Studies.

Pressure reconstruction by eliminating the demand effect of spontaneous respiration (PREDATOR) method for assessing respiratory mechanics of reverse-triggered breathing cycles

Geoffrey M. Shaw, Yeong Shiong Chiew, Vincent Major, 2014, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES).

The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management

Yeong Shiong Chiew, Thomas Desaive, Vincent Major, 2014, BioMedical Engineering OnLine.

Reformulation of the pressure-dependent recruitment model (PRM) of respiratory mechanics

J. Geoffrey Chase, Yeong Shiong Chiew, Knut Möller, 2014, Biomed. Signal Process. Control..

Optimising mechanical ventilation through model-based methods and automation

J. Geoffrey Chase, Merryn H. Tawhai, Knut Möller, 2019, Annual Reviews in Control.

A polynomial model of patient-specific breathing effort during controlled mechanical ventilation

J. Geoffrey Chase, Yeong Shiong Chiew, Daniel P. Redmond, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Estimating the true respiratory mechanics during asynchronous pressure controlled ventilation

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2016, Biomed. Signal Process. Control..

Comparison between single compartment model and recruitment basis function model on NICU patients

Kyeong Tae Kim, J. Chase, J. Knopp, 2020 .

Physiological sex differences in mechanically ventilated premature neonates: A pilot study

Kyeong Tae Kim, J. Chase, J. Knopp, 2020 .

Expiratory model-based method to monitor ARDS disease state

Yeong Shiong Chiew, Thomas Desaive, J Geoffrey Chase, 2013, BioMedical Engineering OnLine.

Positive end expiratory pressure in patients with acute respiratory distress syndrome - The past, present and future

J. Geoffrey Chase, Ashwath Sundaresan, J. Chase, 2012, Biomed. Signal Process. Control..

Performance of lung recruitment model in healthy anesthetised pigs

J. Chase, G. Shaw, B. Lambermont, 2012 .

A minimal model of lung mechanics and model-based markers for optimizing ventilator treatment in ARDS patients

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2009, Comput. Methods Programs Biomed..

Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2005, Comput. Methods Programs Biomed..

Quantifying patient spontaneous breathing effort using model-based methods

Jennifer Knopp, J. Geoffrey Chase, Kyeong Tae Kim, 2021, Biomed. Signal Process. Control..

Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation

J. Geoffrey Chase, Cong Zhou, Qianhui Sun, 2020, Biomed. Signal Process. Control..

Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function

J. Chase, G. Shaw, K. Möller, 2019, Annals of Biomedical Engineering.

Clinical application of respiratory elastance (CARE trial) for mechanically ventilated respiratory failure patients: a model-based study

Yeong Shiong Chiew, Mohd Basri Mat Nor, Ganesaramachandran Arunachalam, 2018 .

Extrapolation of a non-linear autoregressive model of pulmonary mechanics.

Yeong Shiong Chiew, Paul D Docherty, J. Chase, 2017, Mathematical biosciences.

Use of basis functions within a non-linear autoregressive model of pulmonary mechanics

J. Geoffrey Chase, Yeong Shiong Chiew, Knut Möller, 2016, Biomed. Signal Process. Control..

Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2016, Biomed. Signal Process. Control..

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

J. G. Chase, Yeong Shiong Chiew, Paul D. Docherty, 2015 .

Covariance Analysis for Practical Identifiability of an Alveolar Recruitment Model

C Schranz, K Moeller, P D Docherty, 2012, Biomedizinische Technik. Biomedical engineering.

Why Evidence Based Medicine May Be Bad for You and Your Patients

J. Chase, G. Shaw, Fjficm, 2007 .

Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

Geoffrey M. Shaw, Jennifer L. Knopp, J. Geoffrey Chase, 2021, Comput. Methods Programs Biomed..

Analysing the effects of cold, normal, and warm digits on transmittance pulse oximetry

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2016, Biomed. Signal Process. Control..

Peripheral venous blood oxygen saturation can be non-invasively estimated using photoplethysmography

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

NAVA enhances tidal volume and diaphragmatic electro-myographic activity matching: a Range90 analysis of supply and demand

Katherine T. Moorhead, Yeong Shiong Chiew, Thomas Desaive, 2013, Journal of Clinical Monitoring and Computing.

Modelling acute renal failure using blood and breath biomarkers in rats

Christopher E. Hann, J. Geoffrey Chase, Katherine T. Moorhead, 2011, Comput. Methods Programs Biomed..

Model-based cardiac diagnosis of pulmonary embolism

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2007, Comput. Methods Programs Biomed..

Integral-based identification of patient specific parameters for a minimal cardiac model

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2006, Comput. Methods Programs Biomed..

IDENTIFICATION OF TIME VARYING CARDIAC DISEASE STATE USING A MINIMAL CARDIAC MODEL WITH REFLEX ACTIONS.

Christopher E. Hann, Geoffrey M. Shaw, Steen Andreassen, 2006 .

Experimentally verified minimal cardiovascular system model for rapid diagnostic assistance

J. Geoffrey Chase, Geoffrey M. Shaw, Bram W. Smith, 2005 .

Model-Based Prediction of the Patient-Specific Response to Adrenaline

Thomas Desaive, Christopher E Hann, Christina Starfinger, 2010, The open medical informatics journal.

Model-based identification and diagnosis of a porcine model of induced endotoxic shock with hemofiltration.

T Desaive, J G Chase, G M Shaw, 2008, Mathematical biosciences.

Quantifying misclassification and bias errors due to hierarchical sepsis scores in real-time sepsis diagnosis

J. Geoffrey Chase, Geoffrey M. Shaw, Jacquelyn D. Parente, 2020, Biomed. Signal Process. Control..

Virtual patient trials of a multi-input stochastic model for tight glycaemic control using insulin sensitivity and blood glucose data

J. Geoffrey Chase, Christopher G. Pretty, Shaun M. Davidson, 2020, Biomed. Signal Process. Control..

Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care

B. Szabo, B. Paláncz, J. Chase, 2020 .

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

Thomas Desaive, Balazs Benyo, Shaun Davidson, 2019, BioMedical Engineering OnLine.

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2018, IEEE Transactions on Biomedical Engineering.

Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysis

Balázs Benyó, Kent W. Stewart, Hamish Tomlinson, 2016, Annals of Intensive Care.

External validation and sub-cohort analysis of stochastic forecasting models in NICU cohorts

J. Geoffrey Chase, Geoffrey M. Shaw, Jennifer L. Dickson, 2013, Biomed. Signal Process. Control..

Glycemic Levels in Critically Ill Patients: Are Normoglycemia and Low Variability Associated with Improved Outcomes?

J. Chase, Aaron J. Le Compte, M. Signal, 2012, Journal of diabetes science and technology.

Characterisation of the iterative integral parameter identification method

J. Geoffrey Chase, Paul Docherty, Timothy David, 2011, Medical & Biological Engineering & Computing.

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2011, Comput. Methods Programs Biomed..

Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2008, Comput. Methods Programs Biomed..

Model-based glycaemic control in critical care - A review of the state of the possible

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2006, Biomed. Signal Process. Control..

Association of Neonatal Hypoglycemia With Academic Performance in Mid-Childhood.

J. Chase, J. Alsweiler, Arijit Chakraborty, 2022, JAMA.

Improved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement

J. Chase, G. Shaw, C. Pretty, 2017, Biomedical engineering online.

The state of variability: A vision for descriptors of glycaemia

Tony Zhou, J. Geoffrey Chase, Jennifer L. Knopp, 2019, Annu. Rev. Control..

Monte Carlo analysis of a new model-based method for insulin sensitivity testing

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2008, Comput. Methods Programs Biomed..

Integral-based identification of a physiological insulin and glucose model on euglycaemic clamp trials and IVGTT trials (Invited)

Christopher E. Hann, Steen Andreassen, Thomas Lotz, 2006 .

Evaluation of a Model-Based Hemodynamic Monitoring Method in a Porcine Study of Septic Shock

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2013, Comput. Math. Methods Medicine.

Blood pressure waveform contour analysis for assessing peripheral resistance changes in sepsis

J. Chase, C. Pretty, J. Chase, 2018, BioMedical Engineering OnLine.

A new pinched nonlinear hysteretic structural model for automated creation of digital clones in structural health monitoring

J Geoffrey Chase, Cong Zhou, J. Chase, 2020, Structural Health Monitoring.

36th International Symposium on Intensive Care and Emergency Medicine

R. Pizov, E. Mancini, G. Van den Berghe, 2016, Critical Care.

Quantifying agitation in sedated ICU patients using digital imaging

J. Geoffrey Chase, Geoffrey M. Shaw, Harsha Sirisena, 2004, Comput. Methods Programs Biomed..

Post-intensive care syndrome after a critical COVID-19: cohort study from a Belgian follow-up clinic

J. Chase, B. Lambermont, A. Rousseau, 2021, Annals of Intensive Care.

How should we interpret retrospective blood glucose measurements? Sampling and Interpolation

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2017 .

Minimal haemodynamic system model including ventricular interaction and valve dynamics.

J Geoffrey Chase, Bram W Smith, Roger I Nokes, 2004, Medical engineering & physics.

Classifying algorithms for SIFT-MS technology and medical diagnosis

J. Geoffrey Chase, Katherine T. Moorhead, Jennifer M. Scotter, 2008, Comput. Methods Programs Biomed..

Virtual patients and virtual cohorts: a new way to think about the design and implementation of personalized ICU treatments

Thomas Desaive, Jean-Charles Preiser, J. G. Chase, 2016 .

Metabolic model of autoregulation in the Circle of Willis.

J. Chase, T. David, K. Moorhead, 2006, Journal of biomechanical engineering.

3D models of blood flow in the cerebral vasculature.

T David, J G Chase, J Fink, 2006, Journal of biomechanics.

A simplified model for mitral valve dynamics

Christopher E. Hann, J. Geoffrey Chase, Katherine T. Moorhead, 2013, Comput. Methods Programs Biomed..

Monitoring peripheral blood flow change using transmission photoplethysmography sensor

Lachlan R. McKenzie, J. Chase, G. Shaw, 2016, 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA).

Structural health monitoring of tissue mechanics for non-invasive diagnosis of breast cancer

Cong Zhou, J. Geoffrey Chase, Michael Lee, 2018, Autom..

Silicone phantom validation of breast cancer tumor detection using nominal stiffness identification in digital imaging elasto-tomography (DIET)

Cong Zhou, J. Geoffrey Chase, Geoffrey W. Rodgers, 2018, Biomed. Signal Process. Control..

B-spline modelling of inspiratory drive in NAVA-ventilated patients

Kyeong Tae Kim, J. Chase, G. Shaw, 2021, IFAC-PapersOnLine.

Prediction of sepsis progression in critical illness using artificial neural network

J. Chase, G. Shaw, F. Suhaimi, 2015 .

Development of a model-based clinical sepsis biomarker for critically ill patients

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2011, Comput. Methods Programs Biomed..

Continuous Glucose Monitoring and Trend Accuracy

J Geoffrey Chase, Matthew Signal, Rebecca Gottlieb, 2014, Journal of diabetes science and technology.

Validation of subject-specific cardiovascular system models from porcine measurements

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2013, Comput. Methods Programs Biomed..

Patient specific identification of the cardiac driver function in a cardiovascular system model

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2011, Comput. Methods Programs Biomed..

A subcutaneous insulin pharmacokinetic model for insulin Detemir

J. Geoffrey Chase, Ben van Noorden, Jennifer L. Knopp, 2019, Comput. Methods Programs Biomed..

Endogenous insulin secretion in critically ill patients

J. Chase, C. Pretty, J. Lin, 2012, Critical Care.

Structural Identifiability Analysis of a Cardiovascular 1 System Model I , II 2

J. Chase, P. Dauby, T. Desaive, 2015 .

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2010, Comput. Methods Programs Biomed..

Cardiovascular Modelling and Identification in Septic Shock - Experimental validation

Christopher E. Hann, Geoffrey M. Shaw, Thomas Desaive, 2008 .

Simulating transient ventricular interaction using a minimal cardiovascular system model

J Geoffrey Chase, Roger I Nokes, Bram W. Smith, 2006, Physiological measurement.

A glucose-insulin pharmacodynamic surface modeling validation and comparison of metabolic system models

Christopher E. Hann, J. Geoffrey Chase, Jim I. Mann, 2009, Biomed. Signal Process. Control..

Transient and steady-state euglycemic clamp validation of a model for glycemic control and insulin sensitivity testing.

Dominic S. Lee, Christopher E Hann, Jessica Lin, 2006, Diabetes technology & therapeutics.

Fast normalized cross correlation for motion tracking using basis functions

Christopher E. Hann, J. Geoffrey Chase, Eli E. W. Van Houten, 2006, Comput. Methods Programs Biomed..

Predicting fluid-response, the heart of hemodynamic management: A model-based solution

Geoffrey M. Shaw, Christopher G. Pretty, Thomas Desaive, 2021, Comput. Biol. Medicine.

Assessing Respiratory Mechanics of Reverse-Triggered Breathing Cycles - Case Study of Two Mechanically Ventilated Patients

Geoffrey M. Shaw, Christopher G. Pretty, Yeong Shiong Chiew, 2015 .

Bench-Side Dose Accuracy of an Open-Source Ultra-Low-Cost Insulin-Pump, With Testing Conducted to IEC 60601-2-24

Jake D. Campbell, J. Chase, M. D. de Bock, 2022, Journal of diabetes science and technology.

Digital Twins in Critical Care: What, When, How, Where, Why?

S. Malinen, J. Chase, G. Shaw, 2021, IFAC-PapersOnLine.

Vision-based 3D Surface Motion Capture for the DIET Breast Cancer Screening System

Christopher E. Hann, J. Geoffrey Chase, Richard G. Brown, 2010, 2008 15th International Conference on Mechatronics and Machine Vision in Practice.

High(er) Insulin Sensitivity Rules Out Sepsis in Critical Care

C. Hann, J. Chase, G. Shaw, 2008 .

Structural Identifiability and Practical Applicability of an Alveolar Recruitment Model for ARDS Patients

J. Geoffrey Chase, Yeong Shiong Chiew, Knut Möller, 2012, IEEE Transactions on Biomedical Engineering.

Efficient structural health monitoring for a benchmark structure using adaptive RLS filters

J. Geoffrey Chase, Vincent Begoc, Luciana R. Barroso, 2005 .

Structural Model of the Mitral Valve Included in a Cardiovascular Closed Loop Model. Static and Dynamic Validation

J. G. Chase, Bernard Lambermont, Sabine Paeme, 2011 .

Minimal cardiovascular system model including a physiological description of progressive mitral valve orifice dynamics for studying valve dysfunction

P. Kolh, C. Hann, J. Chase, 2011 .

Mathematical model of the mitral valve and the cardiovascular system, application for studying, monitoring and in the diagnosis of valvular pathologies

Christopher E. Hann, J. Geoffrey Chase, Thomas Desaive, 2010 .

Efficient implementation of non-linear valve law and ventricular interaction dynamics in the minimal cardiac model

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2005, Comput. Methods Programs Biomed..

ESICM 2010 WEDNESDAY SESSIONS 13 October 2010

J. Rello, J. Chase, A. Kouatchet, 2010, Intensive Care Medicine.

Model of 30-s sprint cycling performance: Don’t forget the aerobic contribution!

J. Chase, Hamish A. Ferguson, C. Harnish, 2021, IFAC-PapersOnLine.

Creating smooth SI. B-spline basis function representations of insulin sensitivity

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2018, Biomed. Signal Process. Control..

Changes in Identified, Model-based Insulin Sensitivity can be used to Improve Risk and Variability Forecasting in Glycaemic Control

Geoffrey M. Shaw, Thomas Desaive, Jennifer L. Dickson, 2018 .

Accurate and precise prediction of insulin sensitivity variance in critically ill patients

J. Geoffrey Chase, Paul Docherty, Erin J. Mansell, 2018, Biomed. Signal Process. Control..

An Auto-regressive Model for an Arterial Continuous Glucose Monitoring Sensor

J. Geoffrey Chase, Jennifer L. Dickson, Tony Zhou, 2017 .

Accuracy and optimization of a subcutaneous insulin model for less acute critical care patients

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Stochastic Model Predictive (STOMP) glycaemic control for the intensive care unit: Development and virtual trial validation

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2015, Biomed. Signal Process. Control..

Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: Continuous Glucose Monitoring Devices, Sensor Locations, and Detrended Fluctuation Analysis Methods

J. Chase, G. Shaw, F. Thomas, 2013, Journal of diabetes science and technology.

Impact of variation in patient response on model-based control of glycaemia in critically ill patients

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2013, Comput. Methods Programs Biomed..

Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: CGM Devices, Sensor Locations, and DFA Methods

Geoffrey M. Shaw, J. G. Chase, Felicity Thomas, 2013 .

Asymptotic output tracking in blood glucose control. A case study

Levente Kovács, J. Geoffrey Chase, Balázs Benyó, 2011, IEEE Conference on Decision and Control and European Control Conference.

Impact of glucocorticoids on insulin resistance in the critically ill

J. Geoffrey Chase, Geoffrey M. Shaw, Jacquelyn D. Parente, 2011, Comput. Methods Programs Biomed..

Optimal Tight Glycaemic Control Supported by Differential Geometric Methods

Levente Kovács, Balázs Benyó, J. G. Chase, 2011 .

Robust Tight Glycaemic Control of ICU patients

Levente Kovács, J. Geoffrey Chase, Balázs Benyó, 2011 .

Nonlinear control analysis of an ICU model for tight glycaemic control

Levente Kovács, J. Geoffrey Chase, Balázs Benyó, 2011 .

Continuous Glucose Monitors and the Burden of Tight Glycemic Control in Critical Care: Can They Cure the Time Cost?

Matthew Signal, Geoffrey M Shaw, Christopher G Pretty, 2010, Journal of diabetes science and technology.

Hypoglycemia Detection in Critical Care Using Continuous Glucose Monitors: An in Silico Proof of Concept Analysis

Geoffrey M. Shaw, Christopher G. Pretty, Matthew Signal, 2010, Journal of diabetes science and technology.

Investigating the Applicability of qALPV Modeling to ICU Models for Glycaemic Control

Levente Kovács, Christopher E. Hann, J. Geoffrey Chase, 2010 .

Simulation and initial proof-of-concept validation of a glycaemic regulation algorithm in critical care

Christopher E. Hann, Geoffrey M. Shaw, Thomas Lotz, 2008 .

Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study.

S Andreassen, J G Chase, G M Shaw, 2006, Medical engineering & physics.

A pilot study of the SPRINT protocol for tight glycemic control in critically Ill patients.

Thomas Lotz, Christopher E Hann, Jessica Lin, 2006, Diabetes technology & therapeutics.

Stochastic modelling of insulin sensitivity variability in critical care

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2006, Biomed. Signal Process. Control..

Rethinking glycaemic control in critical illness--from concept to clinical practice change.

C. Hann, J. Chase, G. Shaw, 2006, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2006, Comput. Methods Programs Biomed..

A simple insulin-nutrition protocol for tight glycemic control in critical illness: development and protocol comparison.

Thomas Lotz, Christopher E Hann, Jessica Lin, 2006, Diabetes technology & therapeutics.

A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients.

T Lotz, J G Chase, G M Shaw, 2006, Diabetes technology & therapeutics.

Integral-based identification of a physiological insulin and glucose model on euglycaemic clamp and

Christopher E. Hann, Steen Andreassen, Thomas Lotz, 2006 .

IMPACT OF SYSTEM IDENTIFICATION METHODS IN METABOLIC MODELLING AND CONTROL

J. Geoffrey Chase, Geoffrey M. Shaw, Jessica Lin, 2006 .

Automated insulin infusion trials in the intensive care unit.

Geoffrey M Shaw, J. Chase, G. Shaw, 2004, Diabetes technology & therapeutics.

Long term verification of glucose-insulin regulatory system model dynamics

C.E. Hann, J.G. Chase, J. Lin, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Adaptive Bolus-Based Set-Point Regulation of Hyperglycemia in Critical Care

C. Hann, G. Wake, J. Chase, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

A Patient-Specific Airway Branching Model for Mechanically Ventilated Patients

J. Geoffrey Chase, Yeong Shiong Chiew, Thomas Desaive, 2014, Comput. Math. Methods Medicine.

Virtual patients for mechanical ventilation in the intensive care unit

Geoffrey M. Shaw, Thomas Desaive, Merryn H. Tawhai, 2020, Comput. Methods Programs Biomed..

Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model

J. Chase, G. Shaw, K. Möller, 2021, Comput. Biol. Medicine.

Assessing SB effort via a non-invasive model-based method in mechanically ventilated patients in Malaysian ICU hospital

J. Chase, N. Damanhuri, N. A. Othman, 2019, Indonesian Journal of Electrical Engineering and Computer Science.

An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung

J. Chase, K. Möller, P. Docherty, 2014, IFAC Proceedings Volumes.

Copula modelling of agitation-sedation rating of ICU patients: towards monitoring and alerting tools

J. Chase, Irene Hudson, A. Tursunalieva, 2019 .

Accuracy and performance of continuous glucose monitors in athletes

Matthew Signal, Felicity Thomas, Christopher G. Pretty, 2017, Biomed. Signal Process. Control..

Model-Based Insulin Sensitivity as a Sepsis Diagnostic in Critical Care

Christopher E. Hann, Geoffrey M. Shaw, Amy Blakemore, 2008, Journal of diabetes science and technology.

Multiplicative Surrogate Standard Deviation: A Group Metric for the Glycemic Variability of Individual Hospitalized Patients

J. Chase, G. Umpierrez, S. Braithwaite, 2013, Journal of diabetes science and technology.

Measuring facial grimacing for quantifying patient agitation in critical care

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2007, Comput. Methods Programs Biomed..

J. G. Chase, Christopher E. Hann, Thomas Lotz, 2004 .

DIST: A Novel Test for Measuring Insulin Sensitivity

J. G. Chase, J. John Mann, Paul D. Docherty, 2010 .

Biomedical engineer’s guide to the clinical aspects of intensive care mechanical ventilation

J. Chase, G. Shaw, Y. Chiew, 2018, Biomedical engineering online.

Non-invasive over-distension measurements: data driven vs model-based

J. Chase, G. Shaw, K. Möller, 2022, Journal of Clinical Monitoring and Computing.

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model

J. Chase, G. Shaw, K. Möller, 2022, Biomedical engineering online.

Stochastic integrated model-based protocol for volume-controlled ventilation setting

J. Chase, T. Desaive, Xin Wang, 2022, Biomedical engineering online.

Quantifying ventilator unloading in CPAP ventilation

J. Chase, G. Shaw, J. Knopp, 2022, Comput. Biol. Medicine.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

J. Chase, N. Damanhuri, Xin Wang, 2021, Annals of Biomedical Engineering.

A Nonlinear Hysteretic Model for Automated Prediction of Lung Mechanics during Mechanical Ventilation

J. Chase, J. Knopp, Qianhui Sun, 2020, IFAC-PapersOnLine.

Identification of Asynchronous Effect via Pressure-Volume Loop Reconstruction in Mechanically Ventilated Breathing Waveforms

J. Chase, G. Shaw, K. Möller, 2021, IFAC-PapersOnLine.

Pilot study of model-based estimation of inspiratory driving pressure in CPAP ventilation

J. Chase, J. Knopp, Ella F. S. Guy, 2021, IFAC-PapersOnLine.

Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction.

Chee Pin Tan, Yeong Shiong Chiew, Thomas Desaive, 2018, Computer methods and programs in biomedicine.

A better way to determine sample size to detect changes in length of mechanical ventilation?

J. Chase, D. Redmond, T. Desaive, 2015, Critical Care.

Dynamic functional residual capacity can be estimated using a stress-strain approach

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2011, Comput. Methods Programs Biomed..

Does Facemask Impact Diagnostic During Pulmonary Auscultation?

J. Chase, J. Guiot, T. Desaive, 2021, IFAC-PapersOnLine.

Clinical Utilisation of Respiratory Elastance (CURE): Pilot Trials for the Optimisation of Mechanical Ventilation Settings for the Critically Ill

Geoffrey M. Shaw, Yeong Shiong Chiew, Thomas Desaive, 2014 .

Model-based PEEP titration versus standard practice in mechanical ventilation: a randomised controlled trial

Kyeong Tae Kim, J. Chase, G. Shaw, 2020, Trials.

Visualisation of time-varying respiratory system elastance in experimental ARDS animal models

Yeong Shiong Chiew, Thomas Desaive, Bernard Lambermont, 2014, BMC Pulmonary Medicine.

Quantifying neonatal pulmonary mechanics in mechanical ventilation

Jennifer L. Knopp, Kyeong Tae Kim, Bronwyn Dixon, 2019, Biomed. Signal Process. Control..

Clinical Activity Monitoring System (CATS): An automatic system to quantify bedside clinical activities in the intensive care unit.

J. Chase, G. Shaw, P. Guo, 2016, Intensive & critical care nursing.

Model-based respiratory mechanics to titrate PEEP and monitor disease state for experimental ARDS subjects

J. Geoffrey Chase, Yeong Shiong Chiew, Thomas Desaive, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Respiratory system elastance monitoring during PEEP titration

J. Chase, T. Desaive, G. Shaw, 2012, Critical Care.

Lung Mechanics in Premature infants: Modelling and clinical validation

Kyeong Tae Kim, J. Chase, J. Knopp, 2018 .

One-dimensional and three-dimensional models of cerebrovascular flow.

T David, S M Moore, K T Moorhead, 2005, Journal of biomechanical engineering.

A fast and accurate diagnostic test for severe sepsis using model-based insulin sensitivity and clinical data

J. Chase, J. Lin, G. Shaw, 2010, Critical Care.

A fast generalizable solution method for glucose control algorithms.

C E Hann, P Docherty, J G Chase, 2010, Mathematical biosciences.

The Impact of Parameter Identification Methods on Drug Therapy Control in an Intensive Care Unit

J. Geoffrey Chase, Jos Elfring, Christopher E Hann, 2008, The open medical informatics journal.

Modelling intestinal glucose absorption in premature infants using continuous glucose monitoring data

J. Geoffrey Chase, Balázs Benyó, Jennifer L. Knopp, 2019, Comput. Methods Programs Biomed..

Continuous Stroke Volume Estimation from Aortic Pressure Using Zero Dimensional Cardiovascular Model: Proof of Concept Study from Porcine Experiments

Geoffrey M. Shaw, Shun Kamoi, Yeong Shiong Chiew, 2014, PloS one.

Comparison of identification methods of a time-varying insulin sensitivity parameter in a simulation model of glucose metabolism in the critically ill

J. Geoffrey Chase, Chris Hann, Steen Andreassen, 2009 .

A Subcutaneous Insulin Pharmacokinetic Model for Computer Simulation in a Diabetes Decision Support Role: Validation and Simulation

C. Hann, J. Chase, G. Shaw, 2008, Journal of diabetes science and technology.

Is Mortality Rate of Ventilated Patients With Coronavirus Disease 2019 So High?

J. Chase, B. Lambermont, P. Delanaye, 2021, Critical care medicine.

Positive end-expiratory pressure in acute respiratory distress syndrome; where have we been, where are we going?

J. Chase, M. Cove, Y. Chiew, 2023, Clinical Critical Care.

Density Estimation and Wavelet Thresholding via Bayesian Methods: A Wavelet Probability Band and Related Metrics Approach to Assess Agitation and Sedation in ICU Patients

J. Geoffrey Chase, Andrew D. Rudge, In Kang, 2013 .

Blood glucose model for liver transplantation: Alteration of physiological parameters

J. Geoffrey Chase, Balázs Benyó, Jozsef Homlok, 2016, 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI).

Model-based estimation of physiological parameters in the reperfusion phase of liver transplantation

J. Chase, Z. Benyó, B. Benyó, 2014, IEEE 18th International Conference on Intelligent Engineering Systems INES 2014.

Predicting Pulmonary Distension in a Virtual Patient Model for Mechanical Ventilation

J. Chase, G. Shaw, K. Möller, 2021, IFAC-PapersOnLine.

Modeling the glucose regulatory system in extreme preterm infants

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2011, Comput. Methods Programs Biomed..

Blood Glucose Prediction Using Stochastic Modeling in Neonatal Intensive Care

Geoffrey M. Shaw, Dominic S. Lee, Aaron J. Le Compte, 2010, IEEE Transactions on Biomedical Engineering.

Model-based subcutaneous insulin for glycemic control of pre-term infants in the neonatal intensive care unit

J. Chase, J. Knopp, T. Zhou, 2023, Comput. Biol. Medicine.

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

R. Pizov, E. Mancini, G. Van den Berghe, 2016, Critical Care.

Insulin sensitivity and sepsis score: A correlation between model-based metric and sepsis scoring system in critically ill patients

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2017, Biomed. Signal Process. Control..

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

J. Chase, G. Shaw, T. Desaive, 2019, Biomedical engineering online.

Parameter identification and sedative sensitivity analysis of an agitation-sedation model

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2006, Comput. Methods Programs Biomed..

Physiological modelling of agitation-sedation dynamics including endogenous agitation reduction.

J G Chase, G M Shaw, J. Chase, 2006, Medical engineering & physics.

Physiological modelling of agitation-sedation dynamics.

J G Chase, G M Shaw, J. Chase, 2006, Medical engineering & physics.

Automated logging of inspiratory and expiratory non-synchronized breathing (ALIEN) for mechanical ventilation

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Estimating (unidentifiable) enhanced EGP in glycaemic control modelling: Dancing with minions of the Dark Lord

J. Chase, J. Knopp, J. Ormsbee, 2020 .

Nutrition rates and impact of clinical stoppages for two AGC protocols.

Liam M. Fisk, J. Chase, G. Shaw, 2012 .

Comparison of adaptive and sliding-scale glycaemic control in critical care and the impact of nutritional inputs

Christopher E. Hann, G. M. Shaw, Thomas Lotz, 2005 .

Modelling Intestinal Glucose Absorption using Continuous Glucose Monitor Data

Balázs Benyó, Jennifer L. Dickson, Matthew Signal, 2015 .

Blood pressure waveform contour analysis for assessing peripheral resistance changes in sepsis

J. Chase, C. Pretty, T. Desaive, 2018, Biomedical engineering online.

An Investigation into the Clinical Utility of Transfer Functions between the Aortic and Femoral Pressure Waveforms

J. Chase, T. Desaive, Joel Balmer, 2018 .

Minimally invasive estimation of ventricular dead space volume through use of Frank-Starling curves

Antoine Pironet, Thomas Desaive, J Geoffrey Chase, 2017, PloS one.

Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance

J. Chase, C. Pretty, J. Chase, 2017, Biomedical engineering online.

Estimating Increased EGP During Stress Response in Critically Ill Patients

J. Chase, J. Chase, J. Knopp, 2020, Journal of diabetes science and technology.

Model-based identification of PEEP titrations during different volemic levels

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2008, Comput. Methods Programs Biomed..

Specific validation analysis of stochastic ICING model based estimation of insulin sensitivity profile using clinical data

J. Geoffrey Chase, Christopher G. Pretty, Balázs Benyó, 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

INPATIENT HYPOGLYCEMIC EVENTS IN A COMPARATIVE EFFECTIVENESS TRIAL FOR GLYCEMIC CONTROL IN A HIGH-RISK POPULATION.

A. Rademaker, S. Bagshaw, J. Chase, 2016, Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists.

Workshop A: Measurement of Glucose, Insulin, and Ketones—Panel 1: Measurement of Glucose Moderators

D. Klonoff, J. Chase, B. Najafi, 2023 .

Impact of haemodialysis on insulin sensitivity of acute renal failure (ARF) patients with sepsis in critical care

J. Geoffrey Chase, Geoffrey M. Shaw, Paul Docherty, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Observation of changes in model-based insulin sensitivity during haemodialysis transitions for critically ill patients

J. Chase, G. Shaw, A. Compte, 2012 .

An adaptive clinical Type 1 diabetes control protocol to optimize conventional self‐monitoring blood glucose and multiple daily‐injection therapy

Christopher E. Hann, Geoffrey M. Shaw, Jessica Lin, 2008 .

Development of a Clinical Type 1 Diabetes Metabolic System Model and in Silico Simulation Tool

Christopher E. Hann, Geoffrey M. Shaw, Jessica Lin, 2008, Journal of diabetes science and technology.

A finite element model for insulin adsorption in ICU infusion sets

J. Geoffrey Chase, Jennifer L. Knopp, Kaia Bishop, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Modelling insulin adsorption in intravenous infusion sets in the intensive care unit

Anna R. Hardy, Sarah L. Vergeer, J. Chase, 2019, IFAC J. Syst. Control..

Nutrition delivery of a model-based ICU glycaemic control system

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2018, Annals of Intensive Care.

Is there more to glycaemic control than meets the eye?

J Geoffrey Chase, Geoffrey M Shaw, J. Chase, 2007, Critical care.

Shedding light on grey noise in diabetes modelling

J. Geoffrey Chase, Paul Docherty, Erin J. Mansell, 2017, Biomed. Signal Process. Control..

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

Yeong Shiong Chiew, Antoine Pironet, Thomas Desaive, 2018, BioMedical Engineering OnLine.

Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem

B. Paláncz, J. Chase, B. Benyó, 2023, Comput. Methods Programs Biomed..

Nutrition delivery, workload and performance in a model-based ICU glycaemic control system

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2018, Comput. Methods Programs Biomed..

STAR Development and Protocol Comparison

J. Geoffrey Chase, Geoffrey M. Shaw, Thomas Desaive, 2012, IEEE Transactions on Biomedical Engineering.

Characterisation of the iterative integral parameter identification method

J. Chase, T. David, P. Docherty, 2012, Medical and Biological Engineering and Computing.

A Spectrum of Dynamic Insulin Sensitivity Test Protocols

Paul D Docherty, Geoffrey M Shaw, Juliet E Berkeley, 2011, Journal of diabetes science and technology.

Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control

Thomas Desaive, J Geoffrey Chase, Logan Ward, 2011, Annals of intensive care.

Development of blood glucose control for extremely premature infants

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2011, Comput. Methods Programs Biomed..

Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2011, Comput. Methods Programs Biomed..

Tight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis

C. Hann, J. Chase, A. Lynn, 2011, Comput. Methods Programs Biomed..

Design and Clinical Pilot Testing of the Model-Based Dynamic Insulin Sensitivity and Secretion Test (DISST)

Paul D Docherty, Christopher E Hann, Geoffrey M Shaw, 2010, Journal of diabetes science and technology.

Organ failure and tight glycemic control in the SPRINT study

Thomas Desaive, J Geoffrey Chase, Jean-Charles Preiser, 2010, Critical care.

Estimation of the insulin sensitivity profile for the stochastic variant of the ICING model

Christopher G. Pretty, Kent W. Stewart, Balazs Benyo, 2016, 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES).

Stochastic Simulation and Parameter Estimation of the ICING Model

J. Geoffrey Chase, Balázs Benyó, Christopher G. Pretty, 2016 .

Determining Losses in Jet Injection Subcutaneous Insulin Delivery: A Model-Based Approach

J. Chase, J. Knopp, T. Zhou, 2022, Journal of diabetes science and technology.

Kernel density estimates for sepsis classification

Knut Möller, J Geoffrey Chase, Geoffrey M Shaw, 2019, Comput. Methods Programs Biomed..

Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: A case-study of pulmonary mechanics

J. Geoffrey Chase, Yeong Shiong Chiew, Knut Möller, 2014, Comput. Methods Programs Biomed..

An in-silico proof-of-concept investigation of a combined glucose-insulin bolus quick dynamic insulin sensitivity test

J. Geoffrey Chase, Paul Docherty, J. Chase, 2014, Biomed. Signal Process. Control..

Clinical Validation of the Quick Dynamic Insulin Sensitivity Test

J. Geoffrey Chase, Geoffrey M. Shaw, Jim I. Mann, 2013, IEEE Transactions on Biomedical Engineering.

Using the Adapted Levenberg-Marquardt method to determine the validity of ignoring insulin and glucose data that is affected by mixing

J. Chase, P. Docherty, L. T. Morenga, 2020 .

Virtual Trials with B-Spline Basis Functions and Stochastic Differential Equations

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2014 .

Validation and implementation of low-cost dynamic insulin sensitivity tests

J. G. Chase, J. John Mann, Paul D. Docherty, 2011 .

The Identification of Insulin Saturation Effects During the Dynamic Insulin Sensitivity Test

Christopher E. Hann, J. Geoffrey Chase, Geoffrey M. Shaw, 2010, The open medical informatics journal.

DISTq: An Iterative Analysis of Glucose Data for Low-Cost, Real-Time and Accurate Estimation of Insulin Sensitivity

J. Geoffrey Chase, Paul D Docherty, Thomas Lotz, 2009, The open medical informatics journal.

A C-Peptide-Based Model of Pancreatic Insulin Secretion in Extremely Preterm Neonates in Intensive Care

J. Dickson, J. Chase, C. Pretty, 2016, Journal of diabetes science and technology.

Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control

J. Geoffrey Chase, Geoffrey M. Shaw, Christopher G. Pretty, 2014, Comput. Methods Programs Biomed..

Development and optimisation of stochastic targeted (STAR) glycaemic control for pre-term infants in neonatal intensive care

J. Geoffrey Chase, Geoffrey M. Shaw, Jennifer L. Dickson, 2013, Biomed. Signal Process. Control..

Second pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients

J. Chase, G. Shaw, C. Pretty, 2012, Biomedical engineering online.

Model based insulin absorption into intravenous infusion sets in adult and neonatal intensive care unit’s

Anna R. Hardy, J. Chase, J. Chase, 2018 .

Impact of Human Factors on Clinical Protocol Performance: A Proposed Assessment Framework and Case Examples

J. Geoffrey Chase, Geoffrey M. Shaw, Steen Andreassen, 2008, Journal of diabetes science and technology.

Targeted glycemic reduction in critical care using closed-loop control.

J Geoffrey Chase, Thomas Lotz, Bob Broughton, 2005, Diabetes technology & therapeutics.

Blood glucose modelling and control for pre-term infants

Geoffrey M. Shaw, A. J. LeCompte, J. G. Chase, 2008 .

Adaptive bolus-based targeted glucose regulation of hyperglycaemia in critical care.

J Geoffrey Chase, Thomas Lotz, Bob Broughton, 2005, Medical engineering & physics.

Physiological trend analysis of a novel cardio-pulmonary model during a preload reduction manoeuvre

J. Chase, G. Shaw, T. Desaive, 2022, Comput. Methods Programs Biomed..

Preload & Frank-Starling curves, from textbook to bedside: Clinically applicable non-additionally invasive model-based estimation in pigs

Geoffrey M. Shaw, Christopher G. Pretty, Thomas Desaive, 2021, Comput. Biol. Medicine.

Screening for Diabetic Retinopathy Using Computer Vision and Physiological Markers

Christopher E Hann, Geoffrey M Shaw, Darren Hewett, 2009, Journal of diabetes science and technology.

Insulin sensitivity as a model-based marker for sepsis diagnosis

Geoffrey M. Shaw, Christopher G. Pretty, Fatanah M. Suhaimi, 2015 .

Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics

J. Chase, Ruby Langdon, P. Docherty, 2017, Biomedical engineering online.

Relation of Respiratory System Elastance and Expiratory Time Constant: Are They From the Same Lung?

J. Chase, T. Desaive, N. A. Othman, 2014 .

Diabetic Retinopathy Screening Using Computer Vision

Christopher E. Hann, Geoffrey M. Shaw, James A. Revie, 2009 .

Structural identifiability analysis of a cardiovascular system model.

J. Chase, P. Dauby, T. Desaive, 2016, Medical engineering & physics.

Assessing microcirculation condition in critical illness using the pulse oximeter's concept

J. Chase, G. Shaw, C. Pretty, 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

Practical identifiability analysis of a minimal cardiovascular system model

J. Geoffrey Chase, Antoine Pironet, Thomas Desaive, 2017, Comput. Methods Programs Biomed..

Identifiability analysis of a pressure-depending alveolar recruitment model

Yeong Shiong Chiew, Knut Möller, Paul D. Docherty, 2012 .

Tracking the progression to type 2 diabetes with a proportional-derivative insulin secretion model

J. Chase, J. Chase, N. Damanhuri, 2017 .

Laser doppler vibrometer validation of an optical flow motion tracking algorithm

J. Geoffrey Chase, Christopher G. Pretty, Matthew Signal, 2019, Biomed. Signal Process. Control..

Active insulin infusion using optimal and derivative-weighted control.

Z-H Lam, K-S Hwang, J-Y Lee, 2002, Medical engineering & physics.

Rethinking sedation and agitation management in critical illness.

G C Wake, J G Chase, G M Shaw, 2003, Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

Beat-to-beat estimation of the continuous left and right cardiac elastance from metrics commonly available in clinical settings

P. Kolh, C. Hann, J. Chase, 2012, Biomedical engineering online.