Automatic learning algorithm for the MD-logic artificial pancreas system

Shahar Miller, Revital Nimri, Eran Atlas, Eli A. Grunberg, Moshe Phillip

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Applying real-time learning into an artificial pancreas system could effectively track the unpredictable behavior of glucose-insulin dynamics and adjust insulin treatment accordingly. We describe a novel learning algorithm and its performance when integrated into the MD-Logic Artificial Pancreas (MDLAP) system developed by the Diabetes Technology Center, Schneider Children's Medical Center of Israel, Petah Tikva, Israel. Methods: The algorithm was designed to establish an initial patient profile using open-loop data (Initial Learning Algorithm component) and then make periodic adjustments during closed-loop operation (Runtime Learning Algorithm component). The MDLAP system, integrated with the learning algorithm, was tested in seven different experiments using the University of Virginia/Padova simulator, comprising adults, adolescents, and children. The experiments included simulations using the open-loop and closed-loop control strategy under nominal and varying insulin sensitivity conditions. The learning algorithm was automatically activated at the end of the open-loop segment and after every day of the closed-loop operation. Metabolic control parameters achieved at selected time points were compared. Results: The percentage of time glucose levels were maintained within 70-180 mg/dL for children and adolescents significantly improved when open-loop was compared with day 6 of closed-loop control (P<0.0001) and remained unaltered for the adult group (P=0.11) during nominal conditions. In varying insulin sensitivity conditions, the percentage of time glucose levels were below 70 mg/dL was significantly reduced by approximately sevenfold (P<0.001). These observations were correlated with significant reduction in the Low Blood Glucose Index (P<0.001). Conclusions: The new algorithm was effective in characterizing the patient profiles from open-loop data and in adjusting treatment to provide better glycemic control during closed-loop control in both conditions. These findings warrant corroboratory clinical trials.

Original languageEnglish
Pages (from-to)983-990
Number of pages8
JournalDiabetes Technology and Therapeutics
Volume13
Issue number10
DOIs
StatePublished - 1 Oct 2011

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