What's New

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