Digital Health and Machine Learning Technologies for Blood Glucose
Monitoring and Management of Gestational Diabetes
Abstract
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.