Literature DB >> 26259202

An Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results.

Peter Pesl, Pau Herrero, Monika Reddy, Maria Xenou, Nick Oliver, Desmond Johnston, Christofer Toumazou, Pantelis Georgiou.   

Abstract

This paper presents the architecture and initial usability results of an advanced insulin bolus calculator for diabetes (ABC4D), which provides personalized insulin recommendations for people with diabetes by differentiating between various diabetes scenarios and automatically adjusting its parameters over time. The proposed platform comprises two main components: a smartphone-based patient platform allowing manual input of glucose and variables affecting blood glucose levels (e.g., meal carbohydrate content and exercise) and providing real-time insulin bolus recommendations; and a clinical revision platform to supervise the automatic adaptations of the bolus calculator parameters. The system implements a previously in silico validated bolus calculator algorithm based on case-based reasoning, which uses information from similar past events (i.e., cases) to suggest improved personalized insulin bolus recommendations and automatically learns from new events. Usability of ABC4D was assessed by analyzing the system usage at the end of a six-week pilot study (n = 10). Further feedback on the use of ABC4D has been obtained from each participant at the end of the study from a usability questionnaire. On average, each participant requested 115 ± 21 insulin recommendations, of which 103 ± 28 (90%) were accepted. The clinical revision software proposed a total of 754 case revisions, where 723 (96%) adaptations were approved by a clinical expert and updated in the patient platform.

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Year:  2015        PMID: 26259202     DOI: 10.1109/JBHI.2015.2464088

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  16 in total

Review 1.  Bolus Advisors: Sources of Error, Targets for Improvement.

Authors:  John Walsh; Ruth Roberts; Timothy S Bailey; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2017-07-25

2.  Bolus Calculator Safety Mandates a Need for Standards.

Authors:  John Walsh; Guido Freckmann; Ruth Roberts; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2016-12-20

3.  Decision Support in Diabetes Care: The Challenge of Supporting Patients in Their Daily Living Using a Mobile Glucose Predictor.

Authors:  Carmen Pérez-Gandía; Gema García-Sáez; David Subías; Agustín Rodríguez-Herrero; Enrique J Gómez; Mercedes Rigla; M Elena Hernando
Journal:  J Diabetes Sci Technol       Date:  2018-03

Review 4.  A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps.

Authors:  Nathaniel D Heintzman
Journal:  J Diabetes Sci Technol       Date:  2015-12-20

5.  Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability.

Authors:  Pau Herrero; Jorge Bondia; Oloruntoba Adewuyi; Peter Pesl; Mohamed El-Sharkawy; Monika Reddy; Chris Toumazou; Nick Oliver; Pantelis Georgiou
Journal:  Comput Methods Programs Biomed       Date:  2017-06-01       Impact factor: 5.428

6.  Case-Based Reasoning for Insulin Bolus Advice.

Authors:  Peter Pesl; Pau Herrero; Monika Reddy; Nick Oliver; Desmond G Johnston; Christofer Toumazou; Pantelis Georgiou
Journal:  J Diabetes Sci Technol       Date:  2016-07-09

7.  Design and evaluation of a mobile application to assist the self-monitoring of the chronic kidney disease in developing countries.

Authors:  Alvaro Sobrinho; Leandro Dias da Silva; Angelo Perkusich; Maria Eliete Pinheiro; Paulo Cunha
Journal:  BMC Med Inform Decis Mak       Date:  2018-01-12       Impact factor: 2.796

Review 8.  Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.

Authors:  Martina Vettoretti; Giacomo Cappon; Andrea Facchinetti; Giovanni Sparacino
Journal:  Sensors (Basel)       Date:  2020-07-10       Impact factor: 3.576

Review 9.  Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.

Authors:  Ivan Contreras; Josep Vehi
Journal:  J Med Internet Res       Date:  2018-05-30       Impact factor: 5.428

10.  LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management.

Authors:  Jeremy Beauchamp; Razvan Bunescu; Cindy Marling; Zhongen Li; Chang Liu
Journal:  Sensors (Basel)       Date:  2021-05-10       Impact factor: 3.576

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