Iuliana Neagu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Iuliana Neagu has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Bogdan Neamtu
Clinical Medical Department, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania
1 shared publication - Mihai Octavian Negrea
Medical Clinical Department, Faculty of Medicine, “Lucian Blaga” University, 550024 Sibiu, Romania
1 shared publication
Latest Publications
- Open Access
- 22 October 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (20) , 4388
Predicting Glycemic Control in a Small Cohort of Children with Type 1 Diabetes Using Machine Learning Algorithms
Type 1 diabetes, a chronic condition characterized by insulin deficiency, is associated with various complications and reduced life expectancy and is increasing in global prevalence. Maintaining glycaemic control in children with type 1 diabetes, as reflected by glycated hemoglobin levels (A1C), is a challenging task. The American Association of Diabetes (ADA), the Pediatric Endocrine Society, and the International Diabetes Federation (ISPAD) recommend the adoption of a harmonized A1C of p < 0.01) and elevated A1C levels at onset (β = 3.12, p < 0.01) and yielded an area under the receiver operating characteristic curve (AUROC) of 0.916. Two-step clustering emphasized socioeconomic factors, as well as disease complications and comorbidities, and delineated clusters based on these traits. The classification and regression tree (CART) yielded an AUROC of 0.954, slightly outperforming binary regression, providing a comprehensive view of interactions between disease characteristics, comorbidities, and socioeconomic status. Common to all methods were predictors regarding ketoacidosis episodes, the onset of A1C levels, and family income, signifying their overarching importance in glycaemic control. While logistic regression quantified risk, CART visually elucidated complex interactions and two-step clustering exposed patient subgroups that might require different intervention strategies, highlighting how the complementary nature of these analytical methods can enrich clinical interpretation.
- 22 October 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (20) , 4388
Open AccessPredicting Glycemic Control in a Small Cohort of Children with Type 1 Diabetes Using Machine Learning Algorithms
Type 1 diabetes, a chronic condition characterized by insulin deficiency, is associated with various complications and reduced life expectancy and is increasing in global prevalence. Maintaining glycaemic control in children with type 1 diabetes, as reflected by glycated hemoglobin levels (A1C), is a challenging task. The American Association of Diabetes (ADA), the Pediatric Endocrine Society, and the International Diabetes Federation (ISPAD) recommend the adoption of a harmonized A1C of p < 0.01) and elevated A1C levels at onset (β = 3.12, p < 0.01) and yielded an area under the receiver operating characteristic curve (AUROC) of 0.916. Two-step clustering emphasized socioeconomic factors, as well as disease complications and comorbidities, and delineated clusters based on these traits. The classification and regression tree (CART) yielded an AUROC of 0.954, slightly outperforming binary regression, providing a comprehensive view of interactions between disease characteristics, comorbidities, and socioeconomic status. Common to all methods were predictors regarding ketoacidosis episodes, the onset of A1C levels, and family income, signifying their overarching importance in glycaemic control. While logistic regression quantified risk, CART visually elucidated complex interactions and two-step clustering exposed patient subgroups that might require different intervention strategies, highlighting how the complementary nature of these analytical methods can enrich clinical interpretation.