J. Geoffrey Chase

35570524900

Publications - 10

Advances in invasive and non-invasive glucose monitoring: A review of microwave-based sensors

Balázs Benyó J. Geoffrey Chase K. Kovács Ameer B. Alsultani

Publication Name: Sensors and Actuators Reports

Publication Date: 2025-06-01

Volume: 9

Issue: Unknown

Page Range: Unknown

Description:

Effective and continuous glucose monitoring is critical in managing diabetes, which remains a global health challenge affecting millions. Traditional invasive glucose monitoring methods, although accurate, cause discomfort and are unsuitable for frequent measurements necessary for optimal diabetes management. To overcome these limitations, microwave-based sensors have emerged as promising alternatives, providing both invasive and non-invasive monitoring capabilities. This review critically evaluates recent advancements in microwave-based glucose sensors, emphasizing their design methodologies, sensitivity, accuracy, and clinical applicability. By leveraging unique dielectric properties of blood and tissues affected by glucose concentrations, microwave sensors enable precise and potentially pain-free glucose measurements. Despite significant progress, existing sensor technologies face challenges including limited sensitivity ranges, interference from biological tissues, and practical considerations for clinical adoption. This paper aims to guide researchers and healthcare providers by highlighting recent technological innovations, addressing current limitations, and suggesting directions for future research to advance glucose monitoring technologies towards widespread clinical use.

Open Access: Yes

DOI: 10.1016/j.snr.2025.100332

Comparison of three artificial intelligence methods for predicting 90% quantile interval of future insulin sensitivity of intensive care patients

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Omer S. Alkhafaf Ameer B. Alsultani Á Szlávecz B. Paláncz

Publication Name: IFAC Journal of Systems and Control

Publication Date: 2024-12-01

Volume: 30

Issue: Unknown

Page Range: Unknown

Description:

Insulin dosing of hyperglycemic patients in the intensive care unit (ICU) is a complex and nonlinear clinical control problem. Recent model-based glycemic control protocols predict a patient-specific and time-specific future insulin sensitivity distribution, which defines the future patient state in response to insulin and nutrition inputs. The prediction methods provide a 90% confidence interval for a future insulin sensitivity distribution for a given time horizon, making the prediction problem more specific compared to common prediction problems where the aim is to predict the expected value of the given stochastic parameter. This study proposes three alternative artificial intelligence-based insulin sensitivity prediction methods to improve the prediction accuracy and make prediction parameters better fit the clinical requirements. The proposed prediction methods use different neural network models: a classification deep neural network model, a Mixture Density Network model, and a Quantile Regression-based model. A large patient data set was used to create the neural network models, including 2357 patients and 92646 blood glucose measurements from three clinical sites (Christchurch, New Zealand, Gyula, Hungary, and Liege, Belgium). Prediction accuracy was assessed by statistical metrics expressing clinical requirements, as well as via validated in-silico virtual patient simulations comparing the clinical performance of a proven glycaemic control protocol using the alternative prediction methods to assess impact on glycemic control performance and thus the need for these alternative models.

Open Access: Yes

DOI: 10.1016/j.ifacsc.2024.100284

In-Silico Validation of Insulin Sensitivity Prediction by Neural Network-based Quantile Regression

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Omer S. Alkhafaf Ameer B. Alsultani Alaa N. Roel Petra Pintár Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2024-09-01

Volume: 58

Issue: 24

Page Range: 368-373

Description:

High blood glucose levels and stress-induced hyperglycemia are common issues in intensive care units (ICU). Controlling blood glucose levels is crucial but challenging due to patient-specific variability. This challenge was addressed by developing model-based control protocols, which rely on identifying and predicting the patient-specific metabolic state. This study presents the in-silico simulation-based evaluation of a new artificial neural network-based insulin sensitivity (SI) prediction method. The models were trained on a dataset collected during clinical treatment using the stochastic-targeted (STAR) protocol and evaluated by simulating the clinical interventions on virtual patients created from retrospective clinical data. The results show the new models could be safely applied for SI prediction. Furthermore, the adopted method had very similar accuracy in the overall comparison of cohorts, with only minor differences noted in hypoglycemia events.

Open Access: Yes

DOI: 10.1016/j.ifacol.2024.11.065

Cardiovascular Model Identification Using Neural ODE

Balázs Benyó Ákos Antal J. Geoffrey Chase Bálint Szabó K. Kovács Cong Zhou Liam Murphy James Cushway Nicolas Davey Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2024-09-01

Volume: 58

Issue: 24

Page Range: 374-379

Description:

Acute circulatory failure (ACF) is a clinical syndrome when the heart and circulatory circulation cannot provide adequate blood supply to meet metabolic needs of the organs. ACF affects 30%- 50% of intensive care unit (ICU) patients. Fluid resuscitation is the primary treatment of ACF. However, it fails in a significant proportion (about 50%) of cases due to lack of clinically feasible non-invasive perfusion markers to assess the efficacy of the fluid therapy. Unfortunately, unsuccessful fluid therapy negatively affects patient outcome, increasing ICU length of stay and costs. Recent studies show identifying Stressed Blood Volume (SBV) of the cardiovascular system can be used to assess the potential efficacy of fluid therapy. The development of the diagnostic method requires the identification of the central arterial pressure curve based on the femoral arterial pressure, which is clinically available. This central arterial pressure curve can be used to identify the cardiovascular system parameters. In this study, the main goal was to develop a parameter-identification method for the Tube-load model-based transfer function connecting the femoral and central arterial pressure curve by using the so-called Physics-informed Neural Network methodology, namely the Neural ODE method. The study presents the adaptation of the Neural ODE method to the given parameter identification problem and the validation of the developed identification method. The robustness of the developed identification method was tested and used on a series of measurement data recorded in animal experiments.

Open Access: Yes

DOI: 10.1016/j.ifacol.2024.11.066

Applying NeuralODE-based Cardiovascular Model Identification for Experimental Data Analysis

Balázs Benyó Ákos Antal J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz

Publication Name: Saci 2024 18th IEEE International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 437-442

Description:

Recent model-based diagnostic methods have been found promising to provide non-invasive perfusion markers to assess the efficacy of fluid therapy, the most common treatment method for acute circulatory failure (ACF). The development of these model-based diagnostic methods requires the identification of the central arterial pressure curve based on the femoral arterial pressure. This current study presents improvements of the previously suggested NeuralODE-based identification method by suggesting the use of a physiologically interpretable parameter set of the Tube-load model-based transfer function for the physiological system analysis and suggesting a calculation method decreasing the measurement error-caused uncertainty of the identification parameter, called pulse transfer time.

Open Access: Yes

DOI: 10.1109/SACI60582.2024.10619737

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

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz B. Paláncz

Publication Name: Computer Methods and Programs in Biomedicine

Publication Date: 2023-10-01

Volume: 240

Issue: Unknown

Page Range: Unknown

Description:

Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol – used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia – is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model- based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials.

Open Access: Yes

DOI: 10.1016/j.cmpb.2023.107633

Comparison of Three Artificial Intelligence Methods for Predicting 90% Quantile Interval of Future Insulin Sensitivity of Intensive Care Patients

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2023-07-01

Volume: 56

Issue: 2

Page Range: 2091-2095

Description:

Three alternative artificial intelligence-based insulin sensitivity prediction methods are compared in this study. Insulin sensitivity prediction is an essential step in calculating the optimal treatment options in model-based glycemic control protocol of insulin-dependent intensive care patients. The prediction methods must predict not only the expected value of the insulin sensitivity for a given time horizon but also the 90% confidence interval making the prediction problem more specific compared to the common prediction problems. All of the proposed prediction methods - proposed in our previous publications - use different neural network models: a classification deep neural network model, a Mixture Density Network based model, and a Quantile regression based model. The patent data set used for the development and accuracy assessment is from 3 clinical ICU cohorts, including 820 treatment episodes of 606 patients and 68,631 hours of treatment. To evaluate the efficacy of the prediction in the context of clinical requirements, three metrics are used Success rate, Interval ratio, and I-Score are applied.

Open Access: Yes

DOI: 10.1016/j.ifacol.2023.10.1110

Increasing Patient Specificity of the Recurrent Neural Network Based Insulin Sensitivity Prediction by Transfer Learning

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Á Szlávecz B. Paláncz

Publication Name: Ines 2022 26th IEEE International Conference on Intelligent Engineering Systems 2022 Proceedings

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: 27-32

Description:

Insulin therapy is a frequently applied treatment in intensive care to normalize the patient's blood glucose level increased by stress-induced hyperglycaemia. This therapy is generally referred to as Tight Glycaemic Control (TGC). The STAR (Stochastic-TARgeted) protocol is a TGC which uses the patient's insulin sensitivity (SI) as a key parameter to describe the patient's actual state. Prediction of the future patient's state, i.e. prediction of the patient's future SI value, is a crucial step of the protocol currently implemented by using the so-called Intensive Care INsulin Glucose (ICING) model of the human glucose-insulin system and an associated stochastic model. In our previous studies, we have shown that the Recurrent Neural Network (RNN) models are efficient alternative methods of SI prediction. In this paper, we suggest applying the so-called transfer learning technique to further enhance the accuracy of the SI prediction by using the SI history of the current patient. The paper presents the proposed methodology for applying transfer learning in SI prediction and the evaluation of the method's accuracy by comparing the outcomes with the currently applied solution. Insilico validation using real patients' data is involved in this validation.

Open Access: Yes

DOI: 10.1109/INES56734.2022.9922645

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

Balázs Benyó J. Geoffrey Chase Bálint Szabó K. Kovács Yahia Anane Á Szlávecz B. Paláncz

Publication Name: IFAC Papersonline

Publication Date: 2020-01-01

Volume: 53

Issue: 2

Page Range: 16335-16340

Description:

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 widely applied TGC protocol is the STAR (Stochastic-TARgeted) protocol which uses the insulin sensitivity (SI) for the assessment of the patients state. The patient-specific metabolic variability is managed by the so-called stochastic model allowing the prediction of the 90% confidence interval of the future SI value of the patients. In this paper deep neural network (DNN) based methods (classification DNN and Mixture Density Network) are suggested to implement the patient state prediction. The deep neural networks are trained by using three years of STAR treatment data. The methods are validated by comparing the prediction statistics with the reference data set. The prediction accuracy was also compared with the stochastic model currently used in the clinical practice. The presented results proved the applicability of the neural network based methods for the patient state prediction in the model based clinical treatment. Results suggest that the methods' prediction accuracy was the same or better than the currently used stochastic model. These results are the initial successful step in the validation process of the proposed methods which will be followed by in-silico simulation trials.

Open Access: Yes

DOI: 10.1016/j.ifacol.2020.12.659

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

Balázs Benyó J. Geoffrey Chase Jozsef Homlok Paul Docherty K. Kovács

Publication Name: Ines 2013 IEEE 17th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2013-12-12

Volume: Unknown

Issue: Unknown

Page Range: 65-68

Description:

During liver transplantation (LT) the glucose metabolism is effected by a crucial disturbance. The blood glucose level is extremely hard to control by conventional clinical protocols during this phase. Model based approach can enhance the blood glucose control during the anhepatic phase (absence of liver) and post-anhepatic phase. The physiological constants of validated clinical metabolic model were slightly modified based on previous studies. The model fitting errors and the sufficient capture of the blood glucose (BG) dynamic evinced the applicability of the model. However the sufficient per-patient estimation of endogenous production could more enhance the performance of the model based BG prediction. © 2013 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2013.6632784