Department of Laboratory Medicine, The Third Affiliated Hospital of Zhengzhou University, 7 Kangfu Qian Street, Zhengzhou, 450052, Henan, People's Republic of China.
Zhengzhou Key Laboratory for In Vitro Diagnosis of Hypertensive Disorders of Pregnancy, Zhengzhou, 450052, People's Republic of China.
J Endocrinol Invest. 2024 Sep;47(9):2351-2360. doi: 10.1007/s40618-024-02328-z. Epub 2024 Mar 9.
Gestational diabetes mellitus (GDM) is a serious health concern that affects pregnant women worldwide and can lead to adverse pregnancy outcomes. Early detection of high-risk individuals and the implementation of appropriate treatment can enhance these outcomes.
We conducted a study on a cohort of 3467 pregnant women during their pregnancy, with a total of 5649 clinical and biochemical records collected. We utilized this dataset as our training dataset to develop a web server called GDMPredictor. The GDMPredictor utilizes advanced machine learning techniques to predict the risk of GDM in pregnant women. We also personalize treatment recommendations based on essential biochemical indicators, such as A1MG, BMG, CysC, CO2, TBA, FPG, and CREA. Our assessment of GDMPredictor's effectiveness involved training it on the dataset of 3467 pregnant women and measuring its ability to predict GDM risk using an AUC and auPRC.
GDMPredictor demonstrated an impressive level of precision by achieving an AUC score of 0.967. To tailor our treatment recommendations, we use the GDM risk level to identify higher risk candidates who require more intensive care. The GDMPredictor can accept biochemical indicators for predicting the risk of GDM at any period from 1 to 24 weeks, providing healthcare professionals with an intuitive interface to identify high-risk patients and give optimal treatment recommendations.
The GDMPredictor presents a valuable asset for clinical practice, with the potential to change the management of GDM in pregnant women. Its high accuracy and efficiency make it a reliable tool for doctors to improve patient outcomes. Early identification of high-risk individuals and tailored treatment can improve maternal and fetal health outcomes http://www.bioinfogenetics.info/GDM/ .
妊娠糖尿病（GDM）是一个严重的健康问题，影响全球范围内的孕妇，并可能导致不良的妊娠结局。早期发现高危人群并实施适当的治疗可以改善这些结局。
我们对 3467 名孕妇进行了一项研究，共收集了 5649 份临床和生化记录。我们利用这个数据集作为我们的训练数据集来开发一个名为 GDMPredictor 的网络服务器。GDMPredictor 利用先进的机器学习技术来预测孕妇患 GDM 的风险。我们还根据 A1MG、BMG、CysC、CO2、TBA、FPG 和 CREA 等重要生化指标来个性化治疗建议。我们评估 GDMPredictor 的有效性的方法是在 3467 名孕妇的数据集上训练它，并使用 AUC 和 auPRC 来衡量它预测 GDM 风险的能力。
GDMPredictor 通过实现 0.967 的 AUC 得分，表现出了令人印象深刻的精度。为了定制我们的治疗建议，我们使用 GDM 风险水平来识别需要更密切关注的高风险患者。GDMPredictor 可以接受生化指标，以预测 1 至 24 周任何时间段的 GDM 风险，为医疗保健专业人员提供一个直观的界面来识别高风险患者并提供最佳的治疗建议。
GDMPredictor 为临床实践提供了有价值的资产，有可能改变孕妇 GDM 的管理方式。它的高准确性和效率使其成为医生改善患者结局的可靠工具。早期识别高危人群并进行针对性治疗可以改善母婴健康结局。
