Machine learning and translational approaches to personalised care for women with gestational diabetes

妊娠期糖尿病(GDM)是妊娠期首发或确诊的糖耐量异常，全球患病率为14.0%，是最常见的妊娠疾病之一。随着世界范围内肥胖和孕妇年龄的增加，妊娠期糖尿病的发病率可能会增加，对卫生系统造成压力。妊娠期糖尿病可能会对母亲和胎儿产生不利的短期后果。妊娠期糖尿病妇女发生妊娠并发症和不良新生儿结局的风险更高，如剖腹产、早产、巨大儿、胎龄过大的婴儿、新生儿呼吸窘迫综合征、新生儿黄疸和进入新生儿重症监护室。尽管妊娠期糖尿病通常在出生后消退，但它可能会导致母亲和孩子的长期后果。患有妊娠期糖尿病的妇女患2型糖尿病(T2 DM)的几率比没有GDM的妇女高得多。此外，患有妊娠期糖尿病的妇女患高血压、肥胖症和心血管疾病的风险明显更高。与未接触妊娠期糖尿病的儿童相比，患有妊娠期糖尿病的儿童患肥胖症、心血管疾病和糖耐量异常的风险也更高。虽然适当的检测、治疗和后续行动减少了与妊娠期糖尿病相关的并发症，但也给不堪重负的卫生系统带来了重大负担，因为尚不清楚哪些妇女处于危险之中。风险分层对于有效分配资源和指导针对高危人群的二级预防非常重要，特别是在妊娠期糖尿病日益流行的情况下。虽然已经开发了模型来确定哪些妇女有患妊娠期糖尿病的风险，但很少有人专注于确定哪些患有GDM的妇女有经历短期或长期不良后果的风险。数据驱动的机器学习模型有可能使GDM的管理和治疗个性化，使临床医生摆脱一刀切的方法。该项目旨在利用大数据集和机器学习方法的力量，开发能够根据妊娠期糖尿病患者发生短期和长期不良后果的风险对妊娠期糖尿病妇女进行分层的预测模型。该项目的总体目标是开发和验证数据驱动的模型，以使用GDM健康标记血糖数据集和电子健康记录，根据怀孕期间、出生时和怀孕后不良后果的风险对GDM妇女进行分层。在开发风险分层模型之前，我们的第一步是进行系统的文献回顾。搜索战略将以主要目标为指导，以了解目前可用于预测妊娠期糖尿病妇女不良个体结果的模型。下一步将是巩固临床数据集，并探索它们的局限性。在提取和选择特征和结果之后，我们将探索不同的模型，从经典的统计模型到更复杂的机器学习方法。同时，我将与EMIS Health(行业合作伙伴)的法规、法律和业务开发团队合作，了解如何将在学术环境中开发的产品转化为商业市场。

Gestational diabetes mellitus (GDM), glucose intolerance with first onset or recognition during pregnancy, has a global prevalence of 14.0%, making it one of the most common disorders of pregnancy. With increasing obesity and maternal age worldwide, the incidence of GDM is likely to increase, causing a strain on health systems.Gestational diabetes can have adverse short-term consequences on both the mother and the fetus. Women with GDM are at a higher risk of experiencing pregnancy complications and adverse neonatal outcomes such as caesarian section (C-section), preterm delivery, macrosomia, large for gestational age babies, neonatal respiratory distress syndrome, neonatal jaundice, and admission to a neonatal ICU. Although GDM usually subsides after birth, it may lead to long-term consequences for both the mother and the child. The odds of developing type 2 diabetes (T2DM) are substantially higher for women with GDM than without. Additionally, women with GDM are at a significantly higher risk of hypertension, obesity, and cardiovascular morbidity. Children exposed to GDM also have a higher risk of obesity, cardiovascular morbidity and glucose intolerance compared to unexposed children.While appropriate detection, treatment and follow-up reduce the complications associated with GDM, they also place a significant burden on overwhelmed health systems as it is unclear which women are at risk. Risk stratification is important to effectively allocate resources and direct secondary prevention towards at risk populations, especially with the increasing prevalence of GDM. While models have been developed to identify which women are at risk of developing GDM, few have focused on identifying which women with GDM are at risk of experiencing short or long-term adverse outcomes. Data-driven machine learning models have the potential to personalise GDM management and treatment, allowing clinicians to move away from a one-size-fits-all approach. This project intends to leverage the power of large datasets and machine learning methods to develop prediction models capable of stratifying women with GDM based on their risk of developing short and long-term adverse outcomes of GDM. The overarching aim of the project is to develop and validate data-driven models to stratify women with GDM based on risk of adverse outcomes during pregnancy, at birth, and after pregnancy using the GDm-health tagged glucose dataset and electronic health records. Prior to developing risk stratification models, our first step is to conduct a systematic literature review. The search strategy will be guided by the main objective to understand the current models available to predict poor individual outcomes in women with GDM. The next step would be to consolidate the clinical datasets and explore their limitations. Following the extraction and selection of features and outcomes, we will explore different models ranging from classical statistical models to more elaborate machine learning methods. In parallel, I will work with the regulatory, legal, and business development teams at EMIS Health (industry partner) to learn about how to translate a product developed in an academic setting to commercial markets.