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...
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| Veröffentlicht in: | IEEE reviews in biomedical engineering 2024-01, Vol.17, p.1-19 |
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| creator | Lu, Huiqi Y. Ding, Xiaorong Hirst, Jane E Yang, Yang Yang, Jenny Mackillop, Lucy Clifton, David |
| 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. |
| doi_str_mv | 10.1109/RBME.2023.3242261 |
| format | Article |
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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. 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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. 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| subjects | Blood Blood Glucose Blood Glucose Self-Monitoring - methods Blood glucose sensors Diabetes Diabetes mellitus Diabetes, Gestational - diagnosis Diabetes, Gestational - therapy Digital Health Electronic healthcare Female Geographical locations Gestational diabetes Glucose Glucose monitoring Humans Innovations Learning algorithms Machine Learning Medical personnel Monitoring patient monitoring Patients Pregnancy Pregnancy complications Smartphones Technological change Telemedicine Wearable technology |
| title | Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes |
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