El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1
Type 1 diabetes mellitus (T1DM) is a metabolic condition characterised by persistent hyperglycaemia resulting from insufficient pancreatic insulin synthesis. This forces patients to be aware of their daily blood glucose swings in order to deduce a pattern and anticipate future blood glucose and thus...
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| Materialtyp: | Online |
| Språk: | spanska |
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UMA Editorial (Universidad de Málaga)
2025
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| Länkar: | https://directory.doabooks.org/handle/20.500.12854/161206 |
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| _version_ | 1869523693034536960 |
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| author | Rodríguez Rodríguez, Ignacio Rodríguez, José-Victor Campo Valera, María |
| author_browse | Campo Valera, María Rodríguez Rodríguez, Ignacio Rodríguez, José-Victor |
| author_facet | Rodríguez Rodríguez, Ignacio Rodríguez, José-Victor Campo Valera, María |
| author_sort | Rodríguez Rodríguez, Ignacio |
| collection | Directory of Open Access Books |
| description | Type 1 diabetes mellitus (T1DM) is a metabolic condition characterised by persistent hyperglycaemia resulting from insufficient pancreatic insulin synthesis. This forces patients to be aware of their daily blood glucose swings in order to deduce a pattern and anticipate future blood glucose and thus decide how much insulin should be injected exogenously to keep blood glucose within the target range. This approach often suffers from relatively high inaccuracy, which can be dangerous. However, recent advances in information and communication technologies (ICT) and innovative biosensors that could allow a comprehensive, real-time assessment of patient health offer a new perspective on the treatment of T1DM. In this regard, emerging disruptive technologies such as Big Data, Internet of Medical Things (IoMT), Cloud Computing and Machine Learning (ML) can play an important role in the management of T1DM. In this paper, we provide an explanation of previously published IoMT-based approaches to diabetes management, while also assessing the hurdles that future smart management systems IoMT must overcome.
Finally, we provide an overview of a comprehensive IoMT-based approach to DM1 management that aims to address the limits of previous studies while utilising the disruptive technologies highlighted above. |
| format | Online |
| id | doab-20.500.12854ir-161206 |
| institution | Directory of Open Access Books |
| language | spa |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | UMA Editorial (Universidad de Málaga) |
| publisherStr | UMA Editorial (Universidad de Málaga) |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1612062025-06-10T11:29:33Z El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1 Rodríguez Rodríguez, Ignacio Rodríguez, José-Victor Campo Valera, María Diabetes IoMT Machine Learning Communications Internet of Things Biosensors thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJG Endocrinology::MJGD Medicine: Diabetes Type 1 diabetes mellitus (T1DM) is a metabolic condition characterised by persistent hyperglycaemia resulting from insufficient pancreatic insulin synthesis. This forces patients to be aware of their daily blood glucose swings in order to deduce a pattern and anticipate future blood glucose and thus decide how much insulin should be injected exogenously to keep blood glucose within the target range. This approach often suffers from relatively high inaccuracy, which can be dangerous. However, recent advances in information and communication technologies (ICT) and innovative biosensors that could allow a comprehensive, real-time assessment of patient health offer a new perspective on the treatment of T1DM. In this regard, emerging disruptive technologies such as Big Data, Internet of Medical Things (IoMT), Cloud Computing and Machine Learning (ML) can play an important role in the management of T1DM. In this paper, we provide an explanation of previously published IoMT-based approaches to diabetes management, while also assessing the hurdles that future smart management systems IoMT must overcome. Finally, we provide an overview of a comprehensive IoMT-based approach to DM1 management that aims to address the limits of previous studies while utilising the disruptive technologies highlighted above. Published 2025-06-10T11:29:31Z 2025-06-10T11:29:31Z 2023-03-16 book https://directory.doabooks.org/handle/20.500.12854/161206 spa application/pdf Attribution-NonCommercial-NoDerivatives 4.0 International https://monografias.uma.es/index.php/mumaed/catalog/book/5 UMA Editorial (Universidad de Málaga) 10.24310/mumaedmumaed.5 10.24310/mumaedmumaed.5 258ecd78-df9e-4b9e-a9bd-5920eb4daded 85 open access |
| spellingShingle | Diabetes IoMT Machine Learning Communications Internet of Things Biosensors thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJG Endocrinology::MJGD Medicine: Diabetes Rodríguez Rodríguez, Ignacio Rodríguez, José-Victor Campo Valera, María El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1 |
| title | El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1 |
| title_full | El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1 |
| title_fullStr | El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1 |
| title_full_unstemmed | El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1 |
| title_short | El Internet de las Cosas Médicas (IoMT): Una Revolución Tecnológica aplicable a la Gestión de la Diabetes Mellitus Tipo 1 |
| title_sort | el internet de las cosas medicas iomt una revolucion tecnologica aplicable a la gestion de la diabetes mellitus tipo 1 |
| topic | Diabetes IoMT Machine Learning Communications Internet of Things Biosensors thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJG Endocrinology::MJGD Medicine: Diabetes |
| topic_facet | Diabetes IoMT Machine Learning Communications Internet of Things Biosensors thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJG Endocrinology::MJGD Medicine: Diabetes |
| url | https://directory.doabooks.org/handle/20.500.12854/161206 |
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