39 research outputs found
Modeling and Prediction in Diabetes Physiology
Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction
Subspace-based Identification of a Parallel Kinematic Manipulator Dynamics
This thesis deals with the identification of the dynamics of a Parallel Kinematic Manipulator, namely the Gantry-Tau patented by ABB located in the Robotics lav at LTH, Lund. The approach considered for modelling is subspace-based identification of linear models, where measurements from the robot motion are used to estimate the unknown parameters in the models. Rigid body dynamics and flexible body dynamics are taken into account and a description of the system in terms of a network with spring-damper pairs at the edges, representing the clusters, and masses at the nodes representing the end-effector and the carts, is proposed
Linear Modeling and Prediction in Diabetes Physiology
Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. The development of a prediction engine capable of personalized on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise would therefore be a valuable initiative towards an improved management of the desease. This thesis presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 minutes, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects. In order to address model-based control for blood glucose regulation, low-order, individualized, data-driven, stable, physiological relevant models were identified from a population of 9 T1DM patients data. Model structures include: autoregressive moving average with exogenous inputs (ARMAX) models and state-space models.ARMAX multi-step-ahead predictors were estimated by means of least-squares estimation; next regularization of the autoregressive coefficients was introduced. ARMAX-based predictors and zero-order hold were computed to allow comparison.Finally, preliminary results on subspace-based multi-step-ahead multivariate predictors is presented
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology
This paper presents a novel multi-agent reinforcement learning (RL) approach
for personalized glucose control in individuals with type 1 diabetes (T1D). The
method employs a closed-loop system consisting of a blood glucose (BG)
metabolic model and a multi-agent soft actor-critic RL model acting as the
basal-bolus advisor. Performance evaluation is conducted in three scenarios,
comparing the RL agents to conventional therapy. Evaluation metrics include
glucose levels (minimum, maximum, and mean), time spent in different BG ranges,
and average daily bolus and basal insulin dosages. Results demonstrate that the
RL-based basal-bolus advisor significantly improves glucose control, reducing
glycemic variability and increasing time spent within the target range (70-180
mg/dL). Hypoglycemia events are effectively prevented, and severe hyperglycemia
events are reduced. The RL approach also leads to a statistically significant
reduction in average daily basal insulin dosage compared to conventional
therapy. These findings highlight the effectiveness of the multi-agent RL
approach in achieving better glucose control and mitigating the risk of severe
hyperglycemia in individuals with T1D.Comment: 8 pages, 2 figures, 1 Tabl
Multi-step-ahead Multivariate Predictors: A Comparative Analysis
The focus of this article is to undertake a comparative analysis of multi-step-ahead linear multivariate predictors. The approach considered for the estimation will be based on geometrically reliable linear algebra tools, resorting to subspace identification methods. A crucial issue is quantification of both bias error and variance affecting the estimate of the prediction for increasing values of the look ahead when only a small number of samples is available. No complete theory is available so far, nor sufficient numerical experience. Therefore, the analysis of this paper aims at shading some lights on the topic providing some insights and help to develop some intuitions
Personalized short-term blood glucose prediction in T1DM
Insulin therapy for tight glycemia regulation in T1DM strongly depends on patients ́ daily decisions about insulin delivery adaptations in relation to: health status, current BG, target BG, insulin sensitivity, diet and foreseen activities. A personalized predictor providing near future BG predictions would support the users in the decision-making tasks while letting them maintaining control over their own treatments management
Model Predictive Control (MPC) of an Artificial Pancreas with Data-Driven Learning of Multi-Step-Ahead Blood Glucose Predictors
We present the design and \textit{in-silico} evaluation of a closed-loop
insulin delivery algorithm to treat type 1 diabetes (T1D) consisting in a
data-driven multi-step-ahead blood glucose (BG) predictor integrated into a
Linear Time-Varying (LTV) Model Predictive Control (MPC) framework. Instead of
identifying an open-loop model of the glucoregulatory system from available
data, we propose to directly fit the entire BG prediction over a predefined
prediction horizon to be used in the MPC, as a nonlinear function of past
input-ouput data and an affine function of future insulin control inputs. For
the nonlinear part, a Long Short-Term Memory (LSTM) network is proposed, while
for the affine component a linear regression model is chosen. To assess
benefits and drawbacks when compared to a traditional linear MPC based on an
auto-regressive with exogenous (ARX) input model identified from data, we
evaluated the proposed LSTM-MPC controller in three simulation scenarios: a
nominal case with 3 meals per day, a random meal disturbances case where meals
were generated with a recently published meal generator, and a case with 25
decrease in the insulin sensitivity. Further, in all the scenarios, no
feedforward meal bolus was administered. For the more challenging random meal
generation scenario, the mean standard deviation percent time in the
range 70-180 [mg/dL] was 74.99 7.09 vs. 54.15 14.89, the mean
standard deviation percent time in the tighter range 70-140 [mg/dL] was
47.788.55 vs. 34.62 9.04, while the mean standard deviation
percent time in sever hypoglycemia, i.e., 54 [mg/dl] was 1.003.18 vs.
9.4511.71, for our proposed LSTM-MPC controller and the traditional
ARX-MPC, respectively. Our approach provided accurate predictions of future
glucose concentrations and good closed-loop performances of the overall MPC
controller.Comment: 10 pages, 5 Figures, 2 Table
Infinite Horizon Prediction of Post Prandial Breakfast Glucose Excursion
The objective of the study was to investigate infinite horizon prediction of post prandial blood glucose dynamics after breakfast ingestion using subspace based identification on empirical data