Rapid Model Identification for Online Subcutaneous Glucose Concentration Prediction for New Subjects With Type I Diabetes

Zhao, Chunhui; Yu, Chengxia

doi:10.1109/tbme.2014.2387293

Cited by 40 publications

(20 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The specific details regarding the ARX model can be referred to in our previous work . Our previous work mentioned that the model parameters of the exogenous inputs,

B_{ins} true(q^{- 1} true)

and

B_{meal} true(q^{- 1} true)

, and the bias term,

normalβ

, should be revised when the old model is transferred to new subjects. This is due to the varying physiological functions across subjects.…”

Section: Methodsmentioning

confidence: 99%

“…An ARX model has been successfully used to develop models for the artificial pancreas (AP) . Recently, the idea of model migration was proposed for rapid modeling in glucose prediction by considering a similar model structure, but different model parameters between different individuals. This approach revealed that a prediction model developed from data obtained from one subject can be made valid for a new subject with the proper model parameter adjustment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

Zhao

et al. 2017

AIChE Journal

Self Cite

View full text Add to dashboard Cite

To address the problem of insufficient available modeling data for glucose prediction, as well as modeling burden, a model migration method was developed in a previous work to quickly transfer an old model to a new subject using a simple parameter adjustment. However, this method, which is referred to as first‐order model migration (FOMM), only considers a single order for each exogenous input, and may not produce an optimal model structure for accurate prediction. To overcome this problem, a multiple order model migration (MOMM) algorithm is proposed in this study. For different numbers of modeling samples, including glucose and two exogenous inputs (meal and insulin), the optimal modeling method may be different, and therefore, must be properly determined for each modeling scenario. First, the optimal model order is determined for each input and a multiple order prediction model is used. Then, a MOMM algorithm is developed based on particle swarm optimization (PSO) to simultaneously revise multiple parameters. The multiple order parameters of each input in the old model are quickly customized so that the revised model can be used for new subjects with desirable prediction accuracy. In particular, the influence of the number of modeling samples is analysed to check the applicability of different methods; this analysis determines the appropriate selection guidelines for the optimal model in response to different data sizes. The proposed method was evaluated using thirty in silico subjects and clinical data from seven individuals with Type 1 diabetes mellitus (DM). Overall, the MOMM algorithm presented superior results when the time period for collecting the samples was larger than 10 h (50 samples). In particular, the size of the modeling samples was separated into three different regions by evaluating the glucose prediction performance and the comparison between different algorithms for both in silico and clinical subjects. In Region I, the FOMM method achieves the best performance. In Region II, the MOMM method should be used and the prediction accuracy is superior in Region II in general. With enough samples (Region III), the subject‐dependent model (SM) algorithm can be chosen. The MOMM algorithm is demonstrated to be able to transfer models for new subjects with improved model structure. © 2017 American Institute of Chemical Engineers AIChE J, 64: 822–834, 2018

show abstract

“…The specific details regarding the ARX model can be referred to in our previous work . Our previous work mentioned that the model parameters of the exogenous inputs,

B_{ins} true(q^{- 1} true)

and

B_{meal} true(q^{- 1} true)

, and the bias term,

normalβ

, should be revised when the old model is transferred to new subjects. This is due to the varying physiological functions across subjects.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

Zhao

et al. 2017

AIChE Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…This work utilizes PIMA Indian database for predicting the diabetes by employing Artificial Neural Networks (ANN). In [8], a rapid model detection scheme for online subcutaneous glucose concentration prediction system is proposed for candidates with type I diabetes. This work acquires a model and the parameters are modified by considering the data from new candidates for model updation.…”

Section: Review Of Literaturementioning

confidence: 99%

Big Data Processing System for Diabetes Prediction using Machine Learning Technique

Suvarnamukhi*¹,

Seshashayee²

2019

IJITEE

View full text Add to dashboard Cite

Diabetes is one of the threatening diseases to the entire mankind, though it is not fatal. Irrespective of the presence of several existing approaches for diabetes prediction, big data based diabetes prediction is quite rare. The applicability of the proposed work is wider because, medical records from different sources are extracted and the necessary attributes meant for predicting diabetes alone are processed. The goal of this work is attained by different phases such as data collection, preprocessing, attribute selection and prediction. The diabetes prediction is carried out by Extreme Learning Machine (ELM) classifier. The performance of the proposed approach is analysed by varying the classifiers and the existing approaches in terms of disease prediction accuracy, precision, recall and time consumption. From the experimental results, the efficiency of the work is proven.

show abstract

“…It is worth noting that more sophisticated prediction algorithms, also exploiting other signals like the amount of insulin injected or physical activity, can be employed, e.g., those of Zhao et al [9,12], Zecchin et al [13,41], Turksoy et al [10], Zarkogianni et al [11], and Georga et al [42,43]. …”

Section: The Past: the “Smart” Cgm Sensormentioning

confidence: 99%

“…From a clinical point of view, it has been widely demonstrated that the additional information provided by CGM sensors, when used in conjunction with SMBG data, improves the quality of glucose control [7,8]. From an academic point of view, the availability of CGM data stimulated, over the last 15 years, the development of several CGM-based applications, e.g., algorithms for the prediction of future glucose concentration to generate preventive hypo/hyperglycemic alerts [9,10,11,12,13], for the real-time modulation of the basal insulin administration [14,15,16], and for the detection of faults with glucose sensor–insulin pumps system [17,18,19,20,21]. Even more interesting is that CGM sensors enabled the realization of the artificial pancreas (AP), i.e., a device designed mainly for Type 1 diabetes (T1D), which is aimed at maintaining the BG concentration within the safety range by automatically injecting insulin via an insulin pump controlled by a closed-loop control algorithm [22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

Facchinetti

2016

Sensors

144

View full text Add to dashboard Cite

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones.

show abstract

Rapid Model Identification for Online Subcutaneous Glucose Concentration Prediction for New Subjects With Type I Diabetes

Abstract: The proposed method can be regarded as an effective and economic modeling method instead of repetitive subject-dependent modeling method especially for lack of modeling data.

Cited by 40 publications

References 31 publications

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

Big Data Processing System for Diabetes Prediction using Machine Learning Technique

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

Contact Info

Product

Resources

About