Prediction of gestational diabetes based on nationwide electronic health records

• Published in: Nature medicine Vol. 26; no. 1; pp. 71 - 76
• Main Authors: Artzi, Nitzan Shalom, Shilo, Smadar, Hadar, Eran, Rossman, Hagai, Barbash-Hazan, Shiri, Ben-Haroush, Avi, Balicer, Ran D., Feldman, Becca, Wiznitzer, Arnon, Segal, Eran
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.01.2020; Nature Publishing Group
• Subjects: 631/114/2397; 692/163/2743; 692/499; 692/699/2743/137/1926; 692/700/459/1748; Accuracy; Area Under Curve; Biomedical and Life Sciences; Biomedicine; Cancer Research; Cohort Studies; Diabetes; Diabetes in pregnancy; Diabetes mellitus; Diabetes, Gestational - diagnosis; Diagnosis; Electronic Health Records; Electronic medical records; Electronic records; Female; Gestation; Glucose; Glucose tolerance; Health aspects; Humans; Infectious Diseases; Israel; Learning algorithms; Letter; Machine learning; Mass Screening; Medical records; Metabolic Diseases; Model accuracy; Molecular Medicine; Neurosciences; Population studies; Predictions; Pregnancy; Pregnancy complications; Pregnant women; Prognosis; Reproducibility of Results; Risk analysis; Risk factors; Risk reduction; ROC Curve; Surveys and Questionnaires; Israel
• ISSN: 1078-8956; 1546-170X; 1546-170X
• DOI: 10.1038/s41591-019-0724-8

Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes...