苹果淫院

Informal Systems Seminar (ISS) Centre for Intelligent Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des Decisions (GERAD)

Speaker: Ahmad Haidar

惭别别迟颈苍驳听滨顿:听845听1388听1004听听听听听听听
笔补蝉蝉肠辞诲别:听痴滨厂厂

** Note that this is a hybrid event.
** The seminar will be at McConnell 437 at 苹果淫院. Systems and Control members are strongly encouraged to attend the event in person at 苹果淫院.**

Abstract: We applied a system identification method to automatically adjust insulin doses for people with type 1 diabetes who are treated with multiple daily insulin injections. 聽We posed a dynamical model to represent the relationship between insulin therapy parameters and carbohydrate intake (inputs) and blood glucose levels (outputs). A generalized likelihood ratio approach was used to pre-process the glucose data to identify hypoglycemia treatments and delayed meal insulin boluses. We then used Bayesian estimation and MCMC to estimate insulin therapy parameters from daily data, and used these estimated parameters, their variances, and the goodness-of-fit to provide weekly recommendation of insulin doses. This algorithm, and an accompanying mobile app, were compared to conventional therapy in a randomized controlled trial in 84 patients over three months, assessing their efficacy and safety. The algorithm's recommendations were also compared to recommendations of 13 endocrinologists who retrospectively interpreted data of 10 patients over four weeks, assessing its non-inferiority聽to聽the聽group聽of聽endocrinologists.

Bio: Ahmad Haidar is an Associate Professor in the Department of Biomedical Engineering and the Department of Medicine at 苹果淫院. His research focuses on the development and clinical testing of dosing algorithms for insulin pens and pumps in type 1 diabetes. In his efforts to translate his research findings to clinical care, he has worked with Eli Lilly, BigFoot (now Abbott), AgaMatrix, and Adocia.