21 research outputs found
Challenges and Opportunities in Design of Control Algorithm for Artificial Pancreas
With discovery of the insulin, Type-1 diabetes converted from a fatal and acute to a chronic disease which includes micro-vascular complications which range from Kidney disease to stroke and micro-vascular complications such as retinopathy, nephropathy and neuropathy. Artificial pancreas is a solution to improve the quality of life for people with this very fast growing disease in the world and to reduce the costs. Despite technological advances e.g., in subcutaneous sensors and actuators for insulin injection, modeling of blood glucose dynamics and control algorithms still need significant improvement. In this paper, we investigate challenges and opportunities for development of efficient algorithm for designing robust artificial pancreas. We discuss the state of the art and summarize clinical and in silico assessment results. We contrast conventional integer order system approach with a newly proposed fractal control and summarize its benefits
Sensor Drift Compensation Using Fuzzy Interference System and Sparse-Grid Quadrature Filter in Blood Glucose Control
Diabetes mellitus is a serious chronic condition of the human
metabolism. The development of an automated treatment has reached
clinical phase in the last few years. The goal is to keep the blood glucose
concentration within a certain region with minimal interaction required
by the patient or medical personnel. However, there are still several prac-
tical problems to solve. One of these would be that the available sensors
have significant noise and drift. The latter is rather difficult to manage,
because the deviating signal can cause the controller to drive the glu-
cose concentration out of the safe region even in the case of frequent
calibration. In this study a linear-quadratic-Gaussian (LQG) controller
is employed on a widely used diabetes model and enhanced with an ad-
vanced Sparse-grid quadratic filter and a fuzzy interference system-based
calibration supervisor
Linear Matrix Inequality-based Robust Controller design for Type-1 Diabetes Model
This paper investigates the capabilities of a sophisticate
d robust nonlinear controller
designed directly for a widely known and used high-order non
linear type 1 diabetes (T1DM)
model to lessen the dependency from patient compliance and t
o answer practical requirements
such as avoiding hypoglycaemia. The resulting controller c
an perform adequately in nominal
conditions, but expected to keep this performance even in ex
treme situations, e.g. high
carbohydrate intake, rejecting hypoglycaemic episodes
The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas
Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient
Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People
In the context of time-series forecasting, we propose a LSTM-based recurrent
neural network architecture and loss function that enhance the stability of the
predictions. In particular, the loss function penalizes the model, not only on
the prediction error (mean-squared error), but also on the predicted variation
error.
We apply this idea to the prediction of future glucose values in diabetes,
which is a delicate task as unstable predictions can leave the patient in doubt
and make him/her take the wrong action, threatening his/her life. The study is
conducted on type 1 and type 2 diabetic people, with a focus on predictions
made 30-minutes ahead of time.
First, we confirm the superiority, in the context of glucose prediction, of
the LSTM model by comparing it to other state-of-the-art models (Extreme
Learning Machine, Gaussian Process regressor, Support Vector Regressor).
Then, we show the importance of making stable predictions by smoothing the
predictions made by the models, resulting in an overall improvement of the
clinical acceptability of the models at the cost in a slight loss in prediction
accuracy.
Finally, we show that the proposed approach, outperforms all baseline
results. More precisely, it trades a loss of 4.3\% in the prediction accuracy
for an improvement of the clinical acceptability of 27.1\%. When compared to
the moving average post-processing method, we show that the trade-off is more
efficient with our approach
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children
Research in diabetes, especially when it comes to building data-driven models
to forecast future glucose values, is hindered by the sensitive nature of the
data. Because researchers do not share the same data between studies, progress
is hard to assess. This paper aims at comparing the most promising algorithms
in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory
(LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support
Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and
trained on a population of 10 virtual children from the Type 1 Diabetes
Metabolic Simulator software to predict future glucose values at a prediction
horizon of 30 minutes. The performances of the models are evaluated using the
Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis
(CG-EGA). While most of the models end up having low RMSE, the GP model with a
Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction,
has the lowest. Despite having good RMSE values, we show that the models do not
necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only
the LSTM, SVR and GP-DP models have overall acceptable results, each of them
performing best in one of the glycemia regions