In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

Abstract

Background: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers.

Method: We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis.

Results: For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%.

Conclusions: This in silico study of an AI-based closed loop glucose controller shows that it may be able to improve on the results achieved by currently existing ICU-based PID/MPC controllers. If these results are confirmed in clinical testing, this AI-based controller could be used to create an artificial pancreas system for use in the ICU setting.

Keywords: artificial intelligence; closed loop control; cost savings; glucose; intensive care unit; knowledge-based system.

Conflict of interest statement

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: LD and JD are stock holders in Ideal Medical Technologies Inc.

© 2016 Diabetes Technology Society.

Figures

Figure 1.

Schematic representation of control method used in the simulation study. The knowledge base is formed by the rules in the controller. The inference engine decides which rules apply based on the data in the first box that is presented to the controller. The controller functions in an iterative fashion with a cycle length of 5/10 minutes.

Figure 2.

Data are median values with IQR (25-75).

Figure 3.

Median values with IQR (25-75).

Figure 4.

Percentage of all values for a given simulation that are in the hyperglycemic range (>140 mg/dl).

Figure 5.

Percentage of all values for a given simulation that are in the hypoglycemic range (

Figure 6.

All simulations. For all hypoglycemic values, 95% are > 61 mg/dl. Time 0 values are excluded.

Figure 7.

Percentage of all values for a given simulation that are in the 70-140 mg/dl range.

Figure 8.

Distribution of 11.6 million values for all 3 SEs combined. Peak occurrence is at 87 mg/dl. Time 0 values are excluded.

Figure 9.

All simulations with a starting glucose value of 200 mg/dl. For all simulations, 95% enter the given control range by 170 minutes.

Figure 10.

A composite score based on time in range 70-140 mg/dl, CV, time in hyperglycemic range (>140 mg/dl), and distribution of hypoglycemic values

Figure 11.

Screen shot of simulator control panel. Although all of the Van Herpe model parameters can be adjusted in either a fixed or time variant fashion, for the purposes of this study only insulin sensitivity, insulin half-life, and volume of distribution were adjusted from the baseline parameters.

Figure 12.

Screen shot of simulation with control range 80-120 mg/dl, starting glucose 200 mg/dl, SE 10%, bias 0, and dextrose infusion that starts at 0.5 mg/kg/min and increases by 0.5 mg/kg/min every 3 hours. In the upper panel the white line is glucose and the red line is insulin infusion. In the lower panel the white line is dextrose infusion and the green line is X(t) from Van Herpe model.