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Mobile Application Development for Diabetes Patient

文献类型： 会议论文

第一作者： Lehel Dénes-Fazakas

作者： Lehel Dénes-Fazakas 1 ; Gy?rgy Eigner 2 ; Milán Balázs 3 ; Levente Kovács 4 ; László Szilágyi 4 ; Csaba Potyok 3 ; Barbara Simon 4 ; ádám Hartveg 4 ; Máté Siket 3 ;

作者机构： 1.John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Intelligence Institute, óbuda University, Budapest, Hungary|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, Budapest, Hungary

2.John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Intelligence Institute, óbuda University, Budapest, Hungary

3.University Research and Innovation Center, Physiological Controls Research Center, óbuda University, Budapest, Hungary

4.University Research and Innovation Center, Physiological Controls Research Center, óbuda University, Budapest, Hungary|John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Intelligence Institute, óbuda University, Budapest, Hungary

关键词： Diabetes;Mobile applications;Internet;Glucose;Security;Forecasting;Artificial intelligence

会议名称： IEEE International Symposium on Applied Computational Intelligence and Informatics

主办单位：

页码： 559-564

摘要： The proliferation of mobile applications tailored for individuals with diabetes mellitus is on the rise, revolutionizing the management of this chronic condition. These apps offer diverse modules to address the multifaceted needs of diabetes patients, with logging functions being a common feature. While some apps offer basic logging capabilities, others provide more detailed options, including the recording of physical activity, stress levels, and even geolocation data. Furthermore, advancements in artificial intelligence have enhanced these applications, incorporating predictive modules for forecasting blood glucose levels. Our mobile application distinguishes itself with decision support functions, leveraging ongoing research in physical activity detection and reinforcement learning-based insulin regulation. Emphasizing secure medical data management, robust security measures are integrated into our platform. In this article, we introduce a comprehensive mobile application aimed at effectively managing diabetes patients while providing monetization avenues. Key features include doctor-related functionalities and a blood glucose prediction module tailored to individual parameters. A detailed guide on module utilization and monetization strategies will be presented, along with insights into potential future developments.

分类号： tp3

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