Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Pérez-Gandía, Carmen; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio; Gómez, Emilia; Rigla, Mercedes; Leiva, Alberto de; Hernando, M. Elena

doi:10.1089/dia.2009.0076

Cited by 273 publications

(213 citation statements)

References 13 publications

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“…23,28,35 While it is important to choose the most informative features and the best classification method, the efficacy is governed by The sample-based hypoglycemia detection sensitivity and specificity levels of the CGM device reported in this study were 31% and 98%, respectively. Much higher sensitivities have been reported, ranging from 38% to 67%, whereas similar specificities are reported.…”

Section: Discussionmentioning

confidence: 94%

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

Jensen

Christensen

Tarnow

et al. 2013

J Diabetes Sci Technol

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Background: An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. Method: Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. Results: With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. Conclusions: The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.

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Section: Discussionmentioning

confidence: 94%

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

Jensen

Christensen

Tarnow

et al. 2013

J Diabetes Sci Technol

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“…18 The predictor is trained with CGM dataset sensor files and takes into account inter-and intrapatient variability. The time horizon for glucose prediction is 30 min.…”

Section: The Prba Systemmentioning

confidence: 99%

Artificial Pancreas Using a Personalized Rule-Based Controller Achieves Overnight Normoglycemia in Patients with Type 1 Diabetes

Capel

Rigla

García-Sáez

et al. 2014

Diabetes Technology & Therapeutics

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Objective: This study assessed the efficacy of a closed-loop (CL) system consisting of a predictive rule-based algorithm (pRBA) on achieving nocturnal and postprandial normoglycemia in patients with type 1 diabetes mellitus (T1DM). The algorithm is personalized for each patient's data using two different strategies to control nocturnal and postprandial periods. Research Design and Methods: We performed a randomized crossover clinical study in which 10 T1DM patients treated with continuous subcutaneous insulin infusion (CSII) spent two nonconsecutive nights in the research facility: one with their usual CSII pattern (open-loop [OL]) and one controlled by the pRBA (CL). The CL period lasted from 10 p.m. to 10 a.m., including overnight control, and control of breakfast. Venous samples for blood glucose (BG) measurement were collected every 20 min. Results: Time spent in normoglycemia (BG, 3.9-8.0 mmol/L) during the nocturnal period (12 a.m.-8 a.m.), expressed as median (interquartile range), increased from 66.6% (8.3-75%) with OL to 95.8% (73-100%) using the CL algorithm (P < 0.05). Median time in hypoglycemia (BG, <3.9 mmol/L) was reduced from 4.2% (0-21%) in the OL night to 0.0% (0.0-0.0%) in the CL night (P < 0.05). Nine hypoglycemic events ( <3.9 mmol/L) were recorded with OL compared with one using CL. The postprandial glycemic excursion was not lower when the CL system was used in comparison with conventional preprandial bolus: time in target (3.9-10.0 mmol/L) 58.3% (29.1-87.5%) versus 50.0% (50-100%). Conclusions: A highly precise personalized pRBA obtains nocturnal normoglycemia, without significant hypoglycemia, in T1DM patients. There appears to be no clear benefit of CL over prandial bolus on the postprandial glycemia.

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“…18 In fact, several advisory systems for therapy planning based on blood glucose values have been described since 1990, among them, patient simulators, 19 probabilistic network, 20 case-based reasoning, 13 bolus calculators, 21 automatic monitoring data process, [22][23][24][25] and prediction. 26 The Next Scenario: The Global Agent for Diabetes Care…”

Section: Smart Telemedicine For Supporting Continuous Glucose Monitoringmentioning

confidence: 99%

Smart Telemedicine Support for Continuous Glucose Monitoring: The Embryo of a Future Global Agent for Diabetes Care

Rigla

2011

J Diabetes Sci Technol

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Although current systems for continuous glucose monitoring (CGM) are the result of progressive technological improvement, and although a beneficial effect on glucose control has been demonstrated, few patients are using them. Something similar has happened to telemedicine (TM); in spite of the long-term experience, which began in the early 1980s, no TM system has been widely adopted, and presential visits are still almost the only way diabetologists and patients communicate. The hypothesis developed in this article is that neither CGM nor TM will ever be routinely implemented separately, and their consideration as essential elements for standard diabetes care will one day come from their integration as parts of a telemedical monitoring platform. This platform, which should include artificial intelligence for giving decision support to patients and physicians, will represent the core of a more complex global agent for diabetes care, which will provide control algorithms and risk analysis among other essential functions.

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Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Cited by 273 publications

References 13 publications

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

Artificial Pancreas Using a Personalized Rule-Based Controller Achieves Overnight Normoglycemia in Patients with Type 1 Diabetes

Smart Telemedicine Support for Continuous Glucose Monitoring: The Embryo of a Future Global Agent for Diabetes Care

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