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

妊娠期糖尿病（GDM）是妊娠期首次发病或确诊的葡萄糖耐受不良，全球患病率为14.0%，是妊娠期最常见的疾病之一。随着世界范围内肥胖和孕产妇年龄的增加，GDM的发病率可能会增加，对卫生系统造成压力。妊娠期糖尿病会对母亲和胎儿造成短期的不良后果。患有GDM的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、胎龄大的婴儿、新生儿呼吸窘迫综合征、新生儿黄疸和入住新生儿重症监护病房。虽然GDM通常在出生后消退，但它可能会对母亲和孩子造成长期后果。患有GDM的女性患2型糖尿病（T2DM）的几率明显高于没有GDM的女性。此外，患有GDM的女性患高血压、肥胖和心血管疾病的风险明显更高。与未暴露于GDM的儿童相比，暴露于GDM的儿童也有更高的肥胖、心血管疾病和葡萄糖耐受不良的风险。虽然适当的发现、治疗和随访可减少与GDM相关的并发症，但它们也给不堪重负的卫生系统带来沉重负担，因为尚不清楚哪些妇女面临风险。风险分层对于有效分配资源和针对高危人群的二级预防非常重要，特别是随着GDM患病率的增加。虽然已经建立了模型来确定哪些女性有患GDM的风险，但很少有人关注于确定哪些患有GDM的女性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具有个性化GDM管理和治疗的潜力，使临床医生能够摆脱一刀切的方法。该项目旨在利用大型数据集和机器学习方法的力量，开发能够根据GDM短期和长期不良后果风险对GDM女性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模型，利用GDM健康标记葡萄糖数据集和电子健康记录，根据妊娠、分娩和妊娠后不良后果的风险对GDM妇女进行分层。在建立风险分层模型之前，我们的第一步是进行系统的文献综述。搜索策略的主要目标是了解当前可用于预测GDM女性个体预后不良的模型。下一步将是整合临床数据集并探索其局限性。在特征和结果的提取和选择之后，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）的监管、法律和业务开发团队合作，了解如何将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.