Corrigendum: Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

[This corrects the article DOI: 10.3389/fcdhc.2023.

Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.

Background: Hypoglycemia is the most common adverse consequence of treating diabetes, and is often due to suboptimal patient self-care. Behavioral interventions by health professionals and self-care education helps avoid recurrent hypoglycemic episodes by targeting problematic patient behaviors. This relies on time-consuming investigation of reasons behind the observed episodes, which involves manual interpretation of personal diabetes diaries and communication with patients.

Analysis of real-world capillary blood glucose data to help reduce HbA and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variation.

Aims: To examine real-world capillary blood glucose (CBG) data according to HbA to define proportions of CBG readings at different HbA levels, and evaluate patterns in CBG measurements to suggest areas to focus on with regard to self-management.

Methods: A retrospective analysis stratified 682 adults with type 1 diabetes split into quartiles based on their HbA . The proportions of results in different CBG ranges and associations with HbA were evaluated.

COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework.

This research develops machine learning models equipped with interpretation modules for mortality risk prediction and stratification in cohorts of hospitalised coronavirus disease-2019 (COVID-19) patients with and without diabetes mellitus (DM). To this end, routinely collected clinical data from 156 COVID-19 patients with DM and 349 COVID-19 patients without DM were scrutinised. First, a random forest classifier forecasted in-hospital COVID-19 fatality utilising admission data for each cohort.

Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach.

Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning.

Intelligent Data-Driven Model for Diabetes Diurnal Patterns Analysis.

In type 1 diabetes, diurnal activity routines are influential factors in insulin dose calculations. Bolus advisors have been developed to more accurately suggest doses of meal-related insulin based on carbohydrate intake, according to pre-set insulin to carbohydrate levels and insulin sensitivity factors. These parameters can be varied according to the time of day and their optimal setting relies on identifying the daily time periods of routines accurately.

Classification before regression for improving the accuracy of glucose quantification using absorption spectroscopy.

This work contributes to the improvement of glucose quantification using near-infrared (NIR), mid-infrared (MIR), and combination of NIR and MIR absorbance spectroscopy by classifying the spectral data prior to the application of regression models. Both manual and automated classification are presented based on three homogeneous classes defined following the clinical definition of the glycaemic ranges (hypoglycaemia, euglycaemia, and hyperglycaemia). For the manual classification, partial least squares and principal component regressions are applied to each class separately and shown to lead to improved quantification results compared to when applying the same regression models for the whole dataset.

A Deep Neural Network Application for Improved Prediction of [Formula: see text] in Type 1 Diabetes.

[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that [Formula: see text] estimates can be obtained from 5-12 weeks of daily blood glucose measurements. However, these methods suffer from accuracy limitations when applied to incomplete data with missing periods of measurements.