International Journal of Endocrinology and Metabolism
Research Institute for Endocrine Sciences
Scopus by Title (Ref)
1. Predictive Models for Type 2 Diabetes Mellitus in Han Chinese with Insights into Cross-Population Applicability and Demographic Specific Risk Factors
- Chen Y.E. , et al.
3. Approaches to predict future type 2 diabetes mellitus and chronic kidney disease: A scoping review
- Bußmann A. , et al.
4. Adding social determinants of health to the equation: Development of a cardiometabolic disease staging model using clinical and social determinants of health to predict type 2 diabetes
- Howell C.R. , et al.
5. Prediction Models for Diabetes in Children and Adolescents: A Review
- Cveticanin L. , et al.
6. Identification of antibiotic resistance profiles in diabetic foot infections: A machine learning proof-of-concept analysis
- Carrillo-Larco R.M. , et al.
7. Variable-based probabilistic calibration with binary outcome
- Seto H. , et al.
8. Health Management of Type 2 Diabetes Mellitus and Its Complications: A Machine Learning Algorithm-Based Retrospective Study in Chinese Communities
- Luo X. , et al.
9. Indirect estimation of the prevalence of type 2 diabetes mellitus in the sub-population of Tehran: using non-laboratory risk-score models in Iran
- Azizpour Y. , et al.
10. Tailoring nursing interventions to empower patients: personal coping strategies and self-management in type 2 diabetes care
- Ibrahim A.M. , et al.
11. External validation of Prediabetes Risk Test in Indian population for screening prediabetes
- Aditya Jadhav R. , et al.
12. A meta-analysis of diabetes risk prediction models applied to prediabetes screening
- Liu Y. , et al.
13. Statins and new-onset diabetes in primary prevention setting: an updated meta-analysis stratified by baseline diabetes risk
- Masson W. , et al.
14. Mean age and body mass index at type 2 diabetes diagnosis: Pooled analysis of 56 health surveys across income groups and world regions
- Carrillo-Larco R.M. , et al.
15. The Semmelweis Study: a longitudinal occupational cohort study within the framework of the Semmelweis Caring University Model Program for supporting healthy aging
- Ungvari Z. , et al.
16. Predicting diabetes-related conditions in need of intervention: Lolland-Falster Health Study, Denmark
- Lophaven S. , et al.
17. Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data
- Seto H. , et al.