Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.
Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.
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DOI：
10.1109/rbme.2023.3242261
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发表时间：
2024
影响因子：
17.6
通讯作者：
Clifton DA
中科院分区：
文献类型：
--
作者：
Lu HY;Ding X;Hirst JE;Yang Y;Yang J;Mackillop L;Clifton DA
数字健康和机器学习的创新正在改变临床健康和护理的道路。来自不同地理位置和文化背景的人们可以受益于可穿戴设备和智能手机的移动性，以无处不在地监测他们的健康状况。本文重点回顾了妊娠糖尿病（妊娠期间发生的糖尿病的一种亚型）中使用的数字健康和机器学习技术。本文回顾了临床和商业环境中血糖监测设备、数字健康创新以及用于妊娠糖尿病监测和管理的机器学习模型中使用的传感器技术，并讨论了未来的方向。尽管六分之一的母亲患有妊娠期糖尿病，但数字健康应用尚未开发，尤其是可在临床实践中部署的技术。迫切需要（1）为妊娠糖尿病患者开发临床可解释的机器学习方法，协助卫生专业人员在怀孕前、怀孕期间和怀孕后进行治疗、监测和风险分层； (2) 采用和开发经过临床验证的设备，用于患者在家中自我管理健康和福祉（“虚拟病房”和虚拟咨询），从而通过促进及时干预来改善临床结果； (3) 确保具有不同社会经济背景和临床资源的所有女性都能负担得起且可持续的创新。
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.
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DOI：
10.3390/s22134805
发表时间：
2022-06-25
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
通讯作者：
--
影响因子：
3.9
作者：
Bhatia M;Mackillop LH;Bartlett K;Loerup L;Kenworthy Y;Levy JC;Farmer AJ;Velardo C;Tarassenko L;Hirst JE
通讯作者：
Hirst JE
影响因子：
--
作者：
Chen Q;Carbone ET
通讯作者：
Carbone ET
DOI：
10.3389/fgwh.2021.620759
发表时间：
2021
期刊：
Frontiers in global women's health
影响因子：
--
作者：
Nagraj S;Kennedy SH;Jha V;Norton R;Hinton L;Billot L;Rajan E;Arora V;Praveen D;Hirst JE
通讯作者：
Hirst JE