gestational diabetes monitoring and management

Conditions: Gestational Diabetes; Pregnancy Complications; Pregnancy in Diabetic; Pregnancy, High Risk; Diabetes Complications; Pregnancy Induced Hypertension; Pregnancy Weight Gain; Gestational Hypertension; Gestational Weight Gain; Gestational Diabetes Mellitus in Pregnancy; Gestational Complication; Gestational Mother; Pregnancy Preterm; Pregnancy Bleeding; Pregnancy Loss; Birth Weight; Birth Outcome, Adverse; Birth, Preterm; Birth Hypoxia

Summary: the primary goal is to predict the clinical outcomes of mother and baby using blood glucose and other routinely collected clinical data in pregnancy to predict adverse outcomes at birth in women with gdm. the secondary goal is to develop models to predict optimal blood glucose testing schedules for pregnant women. exploratory objectives are (1) to understand patterns of dosage and / or medication choice and (2) to describe different phenotypes of gestational diabetes based on multiple data input.

gestational diabetes is a sub-type of diabetes that causes a person's blood sugar level to become too high during pregnancy. this health condition affects approximately 10% of pregnant women in the uk and up to 20% worldwide. women who have gestational diabetes need to take daily blood tests to monitor their blood sugar. while much work exists on telehealth using blood glucose monitoring, little exists in modern ai-based methods for performing the prediction of patient health status in such settings. this study builds on world-leading research in this field within the institute of biomedical engineering and the nuffield department of women's \& reproductive health at the university of oxford. the focus of this project is to clearly identify patients in different risk groups, predict the clinical outcome of mothers and babies, and reduce the overall number of blood tests. during this study, ci and investigators will develop novel state-of-the-art ai models to improve blood glucose control. this study will use existing retrospective data in pursuit of objectives. the hypothesis in this study is that better blood glucose control will improve clinical outcomes. the predictive models developed in this research study will provide an estimate of patient-specific health risk through time, and notify patients of the clinically appropriate number of blood glucose tests required to monitor their condition. as a result, innovations arising from this study can support future studies to facilitate rapid clinical treatment, transform a hospital-only treatment pathway into a cost-effective home-based alternative, and improve the overall quality of maternal healthcare.

Eligibility criteria:
inclusion criteria: * pregnant women with gdm during pregnancy * record of blood glucose monitoring registered on the gdm-health system exclusion criteria: the participant may not enter the study if any of the following apply: * women who have not consented for their data to be shared through gdm-health * women who opted out of the use of their data in health research

Keywords: gestational diabetes machine learning predictive monitoring clustering patient subtype deep learning transfomer model foundation model diabetes blood glucose c section high risk pregnancy diabetes complications