Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes
Abstract— Innovations in digital health and machine learning are changing the path of clinical health and care. People from many different geographies and cultures can benefit from the mobility of wearable devices and smartphones to monitor their health in a ubiquitous manner. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes ̶ a subtype of diabetes that occurs during pregnancy. Despite a large number of patients with gestational diabetes, only a handful of digital health applications have been deployed in clinical practice. This paper reviews sensor technologies in blood glucose monitoring devices and machine learning fused digital health innovations for gestational diabetes monitoring and management in both clinical and commercial settings. It is one of the first comprehensive reviews in this area to the best of our knowledge. In conclusion, there is a need to (1) develop digital health technologies and clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment monitoring and planning; (2) adapt and develop clinically proven devices for patient self-management of health and well-being at the hospital and home settings thereby facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for women everywhere.
Data statement: this is a review manuscript that have not generated any new data.
The views expressed are those of the authors and not necessarily those of InnoHK. This research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
Huiqi Lu is supported by the Royal Academy of Engineering Daphne Jackson Trust Fellowship Funding, an EPSRC Healthcare Technologies Challenge Award (EP/N020774/1), and an Oxford John Fell Fund (0011028). This work was supported in part by InnoHK Project Programme 3.2: Human Intelligence and AI Integration (HIAI) for the Prediction and Intervention of CVDs: Warning System at Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE).
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- United Kingdom