scholarly journals Vo2 Max Testing Effect On Blood Glucose Level In Adolescents With Type 1 Diabetes

2021 ◽  
Vol 53 (8S) ◽  
pp. 446-446
Author(s):  
Kristi M. King ◽  
Timothy E. McKay ◽  
Bradly J. Thrasher ◽  
Kupper A. Wintergerst
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Testing Effect ◽  
Vo2 Max

Simulation Work for the Control of Blood Glucose Level in Type 1 Diabetes Using Hovorka Equations

2015 ◽  
Vol 1113 ◽  
pp. 739-744 ◽  
Author(s):  
Nur Farhana binti Mohd Yusof ◽  
Ayub Md Som ◽  
Sherif Abdulbari Ali ◽  
Aqilah Liyana binti Abdul Halim Anuar
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Artificial Pancreas ◽  
Parametric Programming ◽  
Diabetes Patient ◽  
Simulation Software ◽  
Time Dynamic

Recently, diabetes is known as one of non-communicable diseases that can lead to fatal if there is no further cure is to be taken especially in South-East Asia regions. An artificial pancreas is introduced to help diabetes patient controls their blood glucose level but the current device is not functioning as fully automated yet. In order to have fully automated artificial pancreas, a controller needs to be improved as the current controller is 33% less accuracy than required. This improvement will help Type 1 diabetes patient in managing their blood glucose level at recommended range. Besides, the presence of controller will help the patient to live normally as non-diabetes people. This research is done to study behaviours of variables in Hovorka model for Type 1 diabetes and to simulate the Hovorka equations. gPROMS software is used due to its speciality in real-time dynamic simulation, fast calculation in complex mathematical equations and capable to adapt multi-parametric programming and Model Predictive Control (MPC). The study is conducted using simulation software based on previous studies experimental data; focusing on the algorithm of the controller. The results illustrate the most active parameter in the model is the administration (bolus & infusion) of insulin.

scholarly journals Early onset type 1 diabetes mellitus in an infant during COVID-19 pandemic: case report

2021 ◽  
Vol 9 (10) ◽  
pp. 3189
Author(s):  
Made Yuliantari Dwi Astiti ◽  
Putu Harrista Indra Pramana ◽  
I. Wayan Bikin Suryawan
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
Early Onset ◽  
White Blood Cells ◽  
Close Monitoring

Type 1 diabetes mellitus (T1DM) is an endocrine disorder, marked by elevated blood glucose level caused by autoimmune process destroying the β-cells of the pancreas which mostly affects children. It is an often-overlooked condition, with low awareness among clinicians and parents alike which led to late diagnosis and patients often presenting with acute complications. Often triggered by a viral infection, here we presented an interesting case of early onset T1DM presenting with Diabetic ketoacidosis (DKA) during a COVID-19 pandemic. A female infant, aged 1 years and 2 days old, presented with dyspnea and fever. Physical examination was otherwise normal, without any rhonchi or wheezing found during pulmonary auscultation. Nasopharyngeal swab and SARS-CoV-2 antigen test was found negative. Laboratory workup found random blood glucose level of 577 mg/dl accompanied by acidosis and ketonuria. The patient also had elevated white blood cells and platelet counts. She was admitted for treatment in the Pediatric intensive care unit (PICU) with therapeutic regiments consisting of slow intravenous insulin infusion, potassium chloride intravenous fluid, antibiotics, and antipyretics. Close monitoring of blood glucose ensues and the patient was treated for 5 days followed by outpatient therapy with mixed insulin treatment twice per day. This case was interesting as T1DM usually manifested in older children with median age of diagnosis ranging from 8 to 13 years old, depending on population. T1DM diagnosed in children younger than 6 years old are classified early onset and it is especially rare to found in infants. Although the patient tested negative for SARS-CoV-2 antigen, the onset of the case coincides with a recent surge of cases locally. It meant that we cannot rule out possibility of prior unknown exposure or infection which may precipitate the condition.

scholarly journals Control of Blood Glucose Level for Type 1 Diabetes Mellitus using Improved Hovorka Equations: Comparison between Clinical and In-Silico Works

2020 ◽  
Author(s):  
Nur’Amanina Mohd Sohadi ◽  
Ayub Md Som ◽  
Noor Shafina Mohd Nor ◽  
Nur Farhana Mohd Yusof ◽  
Sherif Abdulbari Ali ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
In Silico ◽  
Artificial Pancreas ◽  
The Body

AbstractBackgroundType 1 diabetes mellitus (T1DM) occurs due to inability of the body to produce sufficient amount of insulin to regulate blood glucose level (BGL) at normoglycemic range between 4.0 to 7.0 mmol/L. Thus, T1DM patients require to do self-monitoring blood glucose (SMBG) via finger pricks and depend on exogenous insulin injection to maintain their BGL which is very painful and exasperating. Ongoing works on artificial pancreas device nowadays focus primarily on a computer algorithm which is programmed into the controller device. This study aims to simulate so-called improved equations from the Hovorka model using actual patients’ data through in-silico works and compare its findings with the clinical works.MethodsThe study mainly focuses on computer simulation in MATLAB using improved Hovorka equations in order to control the BGL in T1DM. The improved equations can be found in three subsystems namely; glucose, insulin and insulin action subsystems. CHO intakes were varied during breakfast, lunch and dinner times for three consecutive days. Simulated data are compared with the actual patients’ data from the clinical works.ResultsResult revealed that when the patient took 36.0g CHO during breakfast and lunch, the insulin administered was 0.1U/min in order to maintain the blood glucose level (BGL) in the safe range after meal; while during dinner time, 0.083U/min to 0.1 U/min of insulins were administered in order to regulate 45.0g CHO taken during meal. The basal insulin was also injected at 0.066U/min upon waking up time in the early morning. The BGL was able to remain at normal range after each meal during in-silico works compared to clinical works.ConclusionsThis study proved that the improved Hovorka equations via in-silico works can be employed to model the effect of meal disruptions on T1DM patients, as it demonstrated better control as compared to the clinical works.

scholarly journals Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL)

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1164 ◽  
Author(s):  
Rodríguez-Rodríguez ◽  
Rodríguez ◽  
González-Vidal ◽  
Zamora
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Feature Selection ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
Selection Algorithm ◽  
Input Selection

Feature selection is a primary exercise to tackle any forecasting task. Machine learning algorithms used to predict any variable can improve their performance by lessening their computational effort with a proper dataset. Anticipating future glycemia in type 1 diabetes mellitus (DM1) patients provides a baseline in its management, and in this task, we need to carefully select data, especially now, when novel wearable devices offer more and more information. In this paper, a complete characterization of 25 diabetic people has been carried out, registering innovative variables like sleep, schedule, or heart rate in addition to other well-known ones like insulin, meal, and exercise. With this ground-breaking data compilation, we present a study of these features using the Sequential Input Selection Algorithm (SISAL), which is specially prepared for time series data. The results rank features according to their importance, regarding their relevance in blood glucose level prediction as well as indicating the most influential past values to be taken into account and distinguishing features with person-dependent behavior from others with a common performance in any patient. These ideas can be used as strategies to select data for predicting glycemia depending on the availability of computational power, required speed, or required accuracy. In conclusion, this paper tries to analyze if there exists symmetry among the different features that can affect blood glucose levels, that is, if their behavior is symmetric in terms of influence in glycemia.

Reinforcement Learning Based Method for Managing Type 1 Diabetes (Preprint)

2018 ◽  
Author(s):  
Mahsa Oroojeni Mohammad Javad ◽  
Stephen Olusegun Agboola ◽  
Kamal Jethwani ◽  
Ibrahim Zeid ◽  
Sagar Kamarthi
Keyword(s):  
Type 1 Diabetes ◽  
Reinforcement Learning ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Diabetic Patient ◽  
Glucose Level ◽  
Dosage Level ◽  
Insulin Dosage ◽  
Diabetic Patients

BACKGROUND Diabetes is a serious chronic disease marked by high levels of blood glucose. It results from issues related to how insulin is produced and/or how insulin functions in the body. In the long run, uncontrolled blood sugar can damage the vessels that supply blood to important organs such as heart, kidneys, eyes, and nerves. Currently there are no effective algorithms to automatically recommend insulin dosage level considering the characteristics of a diabetic patient. OBJECTIVE The objective of this work is to develop and validate a general reinforcement learning framework and a related learning model for personalized treatment and management of Type 1 diabetes and its complications. METHODS This research presents a model-free reinforcement learning (RL) algorithm to recommend insulin level to regulate the blood glucose level of a diabetic patient considering his/her state defined by A1C level, alcohol usage, activity level, and BMI value. In this approach, an RL agent learns from its exploration and response of diabetic patients when they are subject to different actions in terms of insulin dosage level. As a result of a treatment action at time step t, the RL agent receives a numeric reward depending on the response of the patient’s blood glucose level. At each stage the reward for the learning agent is calculated as a function of the difference between the glucose level in the patient body and its target level. The RL algorithm is trained on ten years of the clinical data of 87 patients obtained from the Mass General Hospital. Demographically, 59% of patients are male and 41% of patients are female; the median of age is 54 years and mean is 52.92 years; 86% of patients are white and 47% of 87 patients are married. RESULTS The performance of the algorithm is evaluated on 60 test cases. Further the performance of Support Vector Machine (SVM) has been applied for Lantus class prediction and results has been compared with Q-learning algorithm recommendation. The results show that the RL recommendations of insulin levels for test patients match with the actual prescriptions of the test patients. The RL gave prediction with an accuracy of 88% and SVM shows 80% accuracy. CONCLUSIONS Since the RL algorithm can select actions that improve patient condition by taking into account delayed effects, it has a good potential to control blood glucose level in diabetic patients.

Characteristics and learning effects of the predictability of the self-monitored blood glucose level in children with type 1 diabetes

2004 ◽  
Vol 65 (2) ◽  
pp. 79-83 ◽  
Author(s):  
Yasuko Uchigata ◽  
Masanao Kawatahara ◽  
Mari Ohsawa ◽  
Junnosuke Miura ◽  
Maki Okudaira ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
The Self ◽  
Learning Effects

BLOOD GLUCOSE LEVEL NEURAL MODEL FOR TYPE 1 DIABETES MELLITUS PATIENTS

2011 ◽  
Vol 21 (06) ◽  
pp. 491-504 ◽  
Author(s):  
ALMA Y. ALANIS ◽  
BLANCA S. LEON ◽  
EDGAR N. SANCHEZ ◽  
EDUARDO RUIZ-VELAZQUEZ
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
Glucose Monitoring ◽  
Nonlinear Behavior ◽  
Neural Model

This paper deals with the blood glucose level modeling for Type 1 Diabetes Mellitus (T1DM) patients. The model is developed using a recurrent neural network trained with an extended Kalman filter based algorithm in order to develop an affine model, which captures the nonlinear behavior of the blood glucose metabolism. The goal is to derive a dynamical mathematical model for the T1DM as the response of a patient to meal and subcutaneous insulin infusion. Experimental data given by continuous glucose monitoring system is utilized for identification and for testing the applicability of the proposed scheme to T1DM subjects.

scholarly journals A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study

JMIR Diabetes ◽  
10.2196/12905 ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. e12905 ◽  
Author(s):  
Mahsa Oroojeni Mohammad Javad ◽  
Stephen Olusegun Agboola ◽  
Kamal Jethwani ◽  
Abe Zeid ◽  
Sagar Kamarthi
Keyword(s):  
Type 1 Diabetes ◽  
Reinforcement Learning ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Clinical Data ◽  
Exploratory Study ◽  
Insulin Dosage

Background Type 1 diabetes mellitus (T1DM) is characterized by chronic insulin deficiency and consequent hyperglycemia. Patients with T1DM require long-term exogenous insulin therapy to regulate blood glucose levels and prevent the long-term complications of the disease. Currently, there are no effective algorithms that consider the unique characteristics of T1DM patients to automatically recommend personalized insulin dosage levels. Objective The objective of this study was to develop and validate a general reinforcement learning (RL) framework for the personalized treatment of T1DM using clinical data. Methods This research presents a model-free data-driven RL algorithm, namely Q-learning, that recommends insulin doses to regulate the blood glucose level of a T1DM patient, considering his or her state defined by glycated hemoglobin (HbA1c) levels, body mass index, engagement in physical activity, and alcohol usage. In this approach, the RL agent identifies the different states of the patient by exploring the patient’s responses when he or she is subjected to varying insulin doses. On the basis of the result of a treatment action at time step t, the RL agent receives a numeric reward, positive or negative. The reward is calculated as a function of the difference between the actual blood glucose level achieved in response to the insulin dose and the targeted HbA1c level. The RL agent was trained on 10 years of clinical data of patients treated at the Mass General Hospital. Results A total of 87 patients were included in the training set. The mean age of these patients was 53 years, 59% (51/87) were male, 86% (75/87) were white, and 47% (41/87) were married. The performance of the RL agent was evaluated on 60 test cases. RL agent–recommended insulin dosage interval includes the actual dose prescribed by the physician in 53 out of 60 cases (53/60, 88%). Conclusions This exploratory study demonstrates that an RL algorithm can be used to recommend personalized insulin doses to achieve adequate glycemic control in patients with T1DM. However, further investigation in a larger sample of patients is needed to confirm these findings.

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