Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes
Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on re...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
IEEE
2023
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| _version_ | 1826312664742100992 |
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| author | Lu, HY Ding, X Hirst, JE Yang, Y Yang, J Mackillop, L Clifton, DA |
| author_facet | Lu, HY Ding, X Hirst, JE Yang, Y Yang, J Mackillop, L Clifton, DA |
| author_sort | Lu, HY |
| collection | OXFORD |
| description | Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes – a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources. |
| first_indexed | 2024-03-07T08:15:42Z |
| format | Journal article |
| id | oxford-uuid:77bd9870-e775-4bef-9bc9-3dc511b97e4d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:57:57Z |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:77bd9870-e775-4bef-9bc9-3dc511b97e4d2024-04-05T10:08:04ZDigital health and machine learning technologies for blood glucose monitoring and management of gestational diabetesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:77bd9870-e775-4bef-9bc9-3dc511b97e4dEnglishSymplectic ElementsIEEE2023Lu, HYDing, XHirst, JEYang, YYang, JMackillop, LClifton, DAInnovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes – a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources. |
| spellingShingle | Lu, HY Ding, X Hirst, JE Yang, Y Yang, J Mackillop, L Clifton, DA Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes |
| title | Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes |
| title_full | Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes |
| title_fullStr | Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes |
| title_full_unstemmed | Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes |
| title_short | Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes |
| title_sort | digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes |
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