Last Posted: Jun 29, 2021
- Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.
van Doorn William P T M et al. PloS one 2021 16(6) e0253125
- Predicting youth diabetes risk using NHANES data and machine learning.
Vangeepuram Nita et al. Scientific reports 2021 11(1) 11212
- Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.
Li Jun et al. International journal of medical informatics 2021 149104429
- A tongue features fusion approach to predicting prediabetes and diabetes with machine learning.
Li Jun et al. Journal of biomedical informatics 2021 Feb 103693
- Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications
MM Baig et al, Appl Clin Informatics, January 2021
- Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes
R Wagner et al, Nature Medicine, January 4, 2021
- PRISQ: a simple risk score to screen for prediabetes: a cross-sectional study from the Qatar Biobank Cohort.
Abbas Mostafa et al. Journal of diabetes investigation 2020 Oct
- Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.
Shindo Kazuhiro et al. Cardiovascular drugs and therapy 2020 May
- A combined strategy of feature selection and machine learning to identify predictors of prediabetes.
De Silva Kushan et al. Journal of the American Medical Informatics Association : JAMIA 2019 Dec
- Family Health History Quiz
Diabetes PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to diabetes...more
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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.