Application of MPC incorporating stochastic programming to type 1 diabetes treatment

Title: Application of MPC incorporating stochastic programming to type 1 diabetes treatment
Creator: Goodwin, Graham C.; Medioli, Adrian M.; Phan, Hieu V.; King, Bruce R.; Matthews, Aaron D.
Relation: American Control Conference (ACC 2016). Proceedings of the American Control Conference (Boston, MA 6-8 July, 2016) p. 907-912
Publisher Link: http://dx.doi.org/10.1109/ACC.2016.7525030
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: conference paper
Date: 2016
Description: This paper describes the application of Model Predictive Control incorporating Stochastic Programming to the treatment of Type 1 diabetes. The use of stochastic programming in this context is believed to be important since it addresses one of the key difficulties associated with diabetes treatment namely the inherent uncertainty associated with future food and exercise patterns. Preliminary results are presented, based on real patients, illustrating the advantages of using stochastic model predictive control for this application.
Subject: insulin; diabetes; stochastic processes; programming; optimization; uncertainty; mathematical model
Identifier: http://hdl.handle.net/1959.13/1340639
Identifier: uon:28535
Identifier: ISBN:9781467386821
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format