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A dynamic risk measure from continuous glucose monitoring data.

Stefania S Guerra, Giovanni G Sparacino, Andrea A Facchinetti, Michele M Schiavon, Chiara Dalla CD Man, and Claudio C Cobelli

Diabetes Technol Ther 13(8):843-52 (2011) PMID 21561370

The quantitative analysis of glucose time-series can greatly help the management of diabetes. In particular, a static nonlinear transformation, which symmetrizes the distribution of glucose levels by bringing them in the so-called risk space, was proposed previously for both self-monitoring blood glucose and continuous glucose monitoring (CGM) and extensively used in the literature. The continuous nature of CGM data allows us to further refine the risk space concept in order to account for glucose dynamics.

DOI: 10.1089/dia.2011.0006
Version: za2963e q8za8 q8zb4 q8zc0 q8zda q8zec q8zfe q8zg3
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