- Diabetes PHGKB -
Last Posted: Feb 26, 2020
- New research directions on disparities in obesity and type 2 diabetes.
Thornton Pamela L et al. Annals of the New York Academy of Sciences 2020 Feb 1461(1) 5-24
- Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis.
Mühlenbruch Kristin et al. Acta diabetologica 2019 Nov
- Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis.
Islam Md Mohaimenul et al. Computer methods and programs in biomedicine 2020 Jan 191105320
- Evidence-based Canadian guidelines for tele-retina screening for diabetic retinopathy: recommendations from the Canadian Retina Research Network (CR2N) Tele-Retina Steering Committee.
Boucher M C et al. Canadian journal of ophthalmology. Journal canadien d'ophtalmologie 2020 Feb 55(1S1) 14-24
- Screening for chronic conditions with reproductive factors using a machine learning based approach.
Tian Siyu et al. Scientific reports 2020 Feb 10(1) 2848
- Aspirin for Prevention of Cardiovascular Disease
AT Peters et al, JAMA Patient Page, February 18, 2020
- Review of current status of molecular diagnosis and characterization of monogenic diabetes mellitus: a focus on next-generation sequencing.
Campbell Michelle Renee et al. Expert review of molecular diagnostics 2020 Feb
- Using Social Media to Track Geographic Variability in Language About Diabetes: Analysis of Diabetes-Related Tweets Across the United States.
Griffis Heather et al. JMIR diabetes 2020 Jan 5(1) e14431
- Parkinsons disease determinants, prediction and gene-environment interactions in the UK Biobank
BM Jacobs, BIORXIV, February 15, 2020
- Genetic profile may predict chance of type 2 diabetes among women with gestational diabetes
NIH News Release, February 13, 2020
- [Italian Cystic Fibrosis Registry (ICFR). Report 2015-2016].
Giordani Barbara et al. Epidemiologia e prevenzione 43(4S1) 1-36
- Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes.
Mueller Luke et al. Diabetes therapy : research, treatment and education of diabetes and related disorders 2020 Feb
- Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning.
Pfaff Emily R et al. JMIR medical informatics 2020 Jan 8(1) e16042
- Diagnosis and Management of Beckwith-Wiedemann Syndrome.
Wang Kathleen H et al. Frontiers in pediatrics 2019 7562
- Impact of hereditary pancreatitis on patients and their families.
Shelton Celeste A et al. Journal of genetic counseling 2020 Feb
- Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.
Aminian Ali et al. Diabetes care 2020 Feb
- The utility of MODY Probability Calculator in probands of families with early-onset autosomal dominant diabetes from Poland.
Hohendorff Jerzy et al. Minerva medica 2019 Dec 110(6) 499-506
- Lifestyle for the prevention of type 2 diabetes: what is the role of genetic risk information?
Chen Guo-Chong et al. The American journal of clinical nutrition 2020 Jan
- Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging.
Hou Ying-Chen Claire et al. Proceedings of the National Academy of Sciences of the United States of America 2020 Jan
- Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.
Cresswell Kathrin et al. Health informatics journal 2020 Jan 1460458219900452
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- Genomic Tests Guidelines (32)
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- Non-Genomics Precision Health (139)
- Pathogen Advanced Molecular Detection (48)
- State Public Health Genomics Programs (40)
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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.