Ensemble methods in combination with compartment models for blood glucose level prediction in type 1 diabetes mellitus

2020 ◽  
Vol 196 ◽  
pp. 105628 ◽  
Author(s):  
Kyriaki Saiti ◽  
Martin Macaš ◽  
Lenka Lhotská ◽  
Kateřina Štechová ◽  
Pavlína Pithová
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
Ensemble Methods ◽  
Compartment Models

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.

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 The fifty two week study of the effectiveness and safety of Apidra insulin therapy in combination with Lantus insulin in the children and adolescents with type 1 diabetes mellitus and poor glycemic control

2014 ◽  
Vol 60 (6) ◽  
pp. 21-28
Author(s):  
Valentina A Peterkova ◽  
Tamara L Kuraeva ◽  
Elena A Andrianova ◽  
Elena V Titovich ◽  
Galina N Svetlova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
Hba1c Level ◽  
Age Group ◽  
Insulin Glargin

The present multi-center non-randomized open prospective phase IV study was carried out based at 8 clinical centers of the Russian Federation. It included 89 patients at the age from 6 to 17 years suffering from type 1 diabetes mellitus during a period over 1 year. The children treated with Lantus (insulin glargin) in combination with any short-acting insulin in accordance with the basal-bolus regime and having the HbA1c level from=>8% to =<10% were transferred to Apidra (insulin glulisine) therapy in combination with Lantus insulin. The number of patients in the first age group (6-12 years) having the HbA1c level <8% within 12 months after the onset of therapy was 51.1%. However, only 31.1% of them, did not experience episodes of symptomatic hypoglycemia during this period with the blood glucose level =< 3.1 mmol/l. In the age group 2 (13-17 years), 31.1% of the patients reached the target HbA1c level <7.5% during the 12 month treatment period, but only 13.3% had no episodes of symptomatic hypoglycemia of =<3.1 mmol/l during this period. The HbA1c level in groups 1 and 2 decreased from 8.75±0.6 to 8.05 ±1.06% (p=0.046658) and from 8.77±0.58 to 7.96±1.12% (p=0.017533) respectively. The requirements for insulin in either group did not significantly change throughout the study period. A total of 1866 hypoglycemic episodes were recorded (i.e. 20.73 episodes per patient) including 90.8% of daytime and 9.2% of nocturnal hypoglycemia; symptomatic hypoglycemia accounted for 98.8% of all the cases and asymptomatic one for 1.2%. Fifty three (0.35%) cases were interpreted as severe hypoglycemia (blood glucose level ≤2 mmol/l), five (0.27%) patients had to be hospitalized . Hypoglycemic episodes were not documented in 13 children.

scholarly journals A Coupled Insulin and Meal Effect Neuro-Fuzzy Model for The Prediction of Blood Glucose Level in Type 1 Diabetes Mellitus Patients.

2019 ◽  
Vol 4 (1) ◽  
pp. 1-15
Author(s):  
N. O. Orieke ◽  
O.S. Asaolu ◽  
T. A. Fashanu ◽  
O. A. Fasanmade
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Level ◽  
Fuzzy Model ◽  
Single Type ◽  
Neuro Fuzzy

AbstractDiabetes Mellitus is a metabolic disorder that affects the ability of the human body to properly utilize and regulate glucose. It is pervasive world-wide yet tenuous and costly to manage. Diabetes Mellitus is also difficult to model because it is nonlinear, dynamic and laden with mostly patient specific uncertainties. A neuro-fuzzy model for the prediction of blood glucose level in Type 1 diabetic patients using coupled insulin and meal effects is developed. This study establishes that the necessary and sufficient conditions to predict blood glucose level in a Type 1 diabetes mellitus patient are: knowledge of the patient’s insulin effects and meal effects under diverse metabolic scenarios and the transparent coupling of the insulin and meal effects. The neuro-fuzzy models were trained with data collected from a single Type 1 diabetic patient covering a period of two months. Clarke’s Error Grid Analysis (CEGA) of the model shows that 87.5% of the predictions fall into region A, while the remaining 12.5% of the predictions fall into region B within a four (4) hour prediction window. The model reveals significant variation in insulin and glucose responses as the Body Mass Index (BMI) of the patient changes.

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