Web application development for diabetes patients

被引：0

作者：

Nagy, Miklos ^{[1
]}

Simon, Barbara ^{[1
,2
]}

Szasz, Laszlo ^{[1
,2
]}

Siket, Mate ^{[1
]}

Denes-Fazakas, Lehel ^{[1
,2
,3
]}

Eigner, Gyorgy ^{[1
,2
]}

Suli, Patrik Peter ^{[1
,2
]}

Kovacs, Levente ^{[1
,2
]}

Szilagyi, Laszlo ^{[1
,2
]}

机构：

[1] Obuda Univ, Univ Res & Innovat Ctr, Physiol Controls Res Ctr, Budapest, Hungary

[2] Obuda Univ, John von Neumann Fac Informat, Biomat & Appl Artificial Intelligence Inst, Budapest, Hungary

[3] Obuda Univ, Appl Informat & Appl Math Doctoral Sch, Becsi St 96-B, H-1034 Budapest, Hungary

来源：

18TH INTERNATIONAL SYMPOSIUM ON APPLIED COMPUTATIONAL INTELLIGENCE AND INFORMATICS, SACI 2024 | 2024年

关键词：

diabetes management; mobile applications; logging functions; insulin intake; carbohydrate intake; artificial intelligence; predictive models; blood glucose levels; decision support; patient monitoring; web application; data security; GLUCOSE CONTROL; MODEL;

D O I：

10.1109/SACI60582.2024.10619716

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The summary provides an overview of the proliferation of diabetes management apps, highlighting their increasing availability and varied features. These apps allow users to log events related to insulin and carbohydrate intake, with some offering additional functionalities like tracking activity levels and stress. Logging capabilities range from basic timestamps to more complex entries with multiple attached data types. Integration of artificial intelligence enhances these apps, enabling predictive models for blood glucose levels and decision support. The summary introduces a web application for monitoring and managing diabetes patients, emphasizing its predictive blood glucose feature based on patient parameters. It includes detailed explanations of module usage and discusses potential future developments, stressing the importance of secure medical data management.

引用

页码：573 / 579

页数：7

共 29 条

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