PDF(Pubmed)
Abstract：
Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.
摘要：
妊娠期糖尿病（GDM）对母亲和婴儿构成重大健康风险。早期预测和有效管理对于改善结果至关重要。机器学习技术已经成为GDM预测的强大工具。这篇综述汇编和分析了现有的研究，以突出机器学习在GDM预测中应用的关键发现和趋势。对2000年至2023年9月发表的相关研究进行了全面搜索。基于对GDM预测的机器学习的关注，选择了14项研究。对这些研究进行了严格的分析，以确定共同的主题和趋势。审查揭示了几个关键主题。从所审查的研究中确定了能够预测妊娠早期GDM风险的模型。一些研究强调了为特定人群和人口群体定制预测模型的必要性。这些发现强调了针对不同人群的统一指南的局限性。此外，研究强调了将临床数据整合到GDM预测模型中的价值.这种整合改善了诊断患有GDM的个体的治疗和护理递送。虽然不同的机器学习模型显示出了希望，选择和称重变量仍然很复杂。审查的研究提供了对使用机器学习进行GDM预测的复杂性和潜在解决方案的宝贵见解。追求准确，早期预测模型，考虑不同的人口，临床资料,和新出现的数据来源强调了研究人员致力于改善有GDM风险的孕妇的医疗结果.
