Artificial Intelligence Predicts Gestational Diabetes in Chinese Women

Machine learning, a form of artificial intelligence, can predict which women are at high risk of developing gestational diabetes and lead to earlier intervention, according to a new study published in The Journal of Clinical Endocrinology & Metabolism.

Gestational diabetes is a common complication during pregnancy that affects up to 15% of pregnant women. High blood sugar in the mother can be dangerous for the baby and lead to complications like stillbirth and premature delivery. Most women are diagnosed with gestational diabetes during the second trimester, but some women are at high risk and could benefit from earlier intervention.

“Our study leveraged artificial intelligence to predict gestational diabetes in the first trimester using electronic health record data from a Chinese hospital,” says study author He-Feng Huang, PhD, of the Shanghai Jiao Tong University School of Medicine and the International Peace Maternity and Child Health Hospital in Shanghai, China. “These findings can help clinicians identify women at high risk of diabetes in early pregnancy and start interventions such as diet changes sooner. The artificial intelligence technology will continue to improve over time and help us better understand the risk factors for gestational diabetes.”

The researchers analyzed nearly 17,000 electronic health records from a hospital in China in 2017 with machine learning models to predict women at high risk for gestational diabetes. They compared their predictions with 2018 electronic health record data and found they were successful at identifying who would develop gestational diabetes. The prediction models also found an association between low body mass and gestational diabetes.

Other authors of the study include: Yan-Ting Wu, Chen-Jie Zhang, Cheng Li, Yu Wang, Jian-Xia Fan, and Lei Chen of the Shanghai Jiao Tong University School of Medicine and the International Peace Maternity and Child Health Hospital; Ben Willem Mol and Andrew Kawai of Monash University in Melbourne, Australia; Jian-Zhong Sheng of the Zhejiang University in Zhejiang, China; and Yi Shi of the Shanghai Jiao Tong University.

The manuscript received funding from the National Key Research and Development Program of China, the National Natural Science Foundation of China, the Foundation of Shanghai Municipal Commission of Health and Family Planning, the Clinical Skills Improvement Foundation of Shanghai Jiaotong University School of Medicine, the Natural Science Foundation of Shanghai, the Shanghai Shenkang Hospital Development Center, Clinical Technology Innovation Project, the Program of Shanghai Academic Research Leader, the CAMS Innovation Fund for Medical Sciences, and the Outstanding Youth Medical Talents of Shanghai Rising Stars of Medical Talent Youth Development Program.

The manuscript, “Early Prediction of Gestational Diabetes Mellitus in the Chinese Population Via Advanced Machine Learning,” was published online, ahead of print.

You may also like

  • Factors Other than Gestational Weight Gain Linked to Metabolic Complications of Pregnancy

    Factors other than how much weight a woman gains during pregnancy may contribute to metabolic complications such as gestational diabetes (GDM), according to a paper recently published in Journal of the Endocrine Society. Researchers led by Fernanda L. Alvarado, MD, of the Mother Infant Research Institute at Tufts Medical Center in Boston, Mass., point out…

  • Phase III Trials Set After Potential Type 1 Diabetes Vaccine Shows Promise

    [Editor’s note: This post has been updated to clarify that the meta-analysis published in Diabetologia and the Phase IIb trial were separate studies.] Earlier this fall, Phase IIb topline results of the DIAGNODE-2 trial demonstrated a potential type 1 diabetes vaccine GAD-alum —  an immunomodulating antigen-specific therapy – had a highly significant and clinically relevant…