TY  - BOOK
AU  - Echouffo-Tcheugui, Justin Basile
TI  - Predictive modeling for incident and prevalent diabetes risk evaluation
SN  - 1744-6651
PY  - 2015///
KW  - PubMed-not-MEDLINE -- Not indexed
KW  - Medstar Union Memorial Hospital
KW  - Journal Article
N2  - With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation
UR  - https://dx.doi.org/10.1586/17446651.2015.1015989
ER  -