Machine leaning models to predict the risk of preterm birth

机器学习模型预测早产风险

• 批准号: 2733975
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2733975
• 关键词: Machine; leaning; models; predict; risk

The Project will:-Cross-validate experimental data from placenta and fetal cell populations (cell mechanics, collagen, inflammatory biomarkers) with computational models to predict the risk of membrane failure to pollution.-Develop a biomarker database of individual characteristics linking clinical data from women (maternal and umbilical cord blood, amniotic fluid, intact placenta) with risk factors taken from women's demographics (gestational age, BMI, postcode location, work/home lifestyle, causes of PPROM).-Use experimental and clinical data to learn relationships and build machine learning tools that will automate analysis and predict women who are at greater risk of PPROM and preterm birth.The integrity of the fetal membranes that surrounds the baby in the womb during pregnancy is vital for normal development. Once the fetal membranes have ruptured or are damaged, they fail to heal leaving a defect until the end of pregnancy. This condition is called pre-term premature rupture of the fetal membranes (PPROM). Exposure to air pollution during pregnancy is associated with PPROM leading to preterm birth. Air pollution results mainly from the combustion of fossil fuels and from industrial emissions and has become a major global public health issue for all people in all age groups. In England, PPROM affects 1 in 9 pregnancies and in London, this increases to 1 in 7 annually. Unicef Executive Director warned the dangers of air pollution to unborn babies. Environmental harm during gestation, infancy and early childhood will have long term impacts across the lifetime. Currently, there are no clinical solutions to improve healing of the fetal membranes after they rupture.

该项目将：-用计算模型交叉验证来自胎盘和胎儿细胞群体(细胞力学、胶原蛋白、炎症生物标记物)的实验数据，以预测膜污染失效的风险。-开发个人特征生物标记物数据库，将妇女的临床数据(母体和脐带血、羊水、完整胎盘)与来自妇女人口统计学的风险因素(孕周、体重指数、邮编位置、工作/家庭生活方式、-使用实验和临床数据来了解关系并建立机器学习工具，这些工具将自动分析和预测哪些妇女面临更高的PPROM和早产风险。怀孕期间围绕着婴儿的胎膜的完整性对正常发育至关重要。一旦胎膜破裂或受损，它们就不能愈合，直到怀孕结束时才会留下缺陷。这种情况称为早产儿胎膜早破(PPROM)。孕期暴露于空气污染与胎膜早破导致早产有关。空气污染主要是由化石燃料的燃烧和工业排放造成的，已成为所有年龄段的所有人的重大全球公共卫生问题。在英格兰，PPROM每年影响1/9的孕妇，在伦敦，这一比例每年增加到1/7。联合国儿童基金会执行主任警告说，空气污染对未出生的婴儿有危险。孕期、婴儿期和幼儿期的环境危害将对人的一生产生长期影响。目前，还没有临床解决方案来改善胎膜破裂后的愈合。