Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jain, Ayush; Shoran, Preety; Vidushi Title A name given to the resource Neonatal Hypoglycemia in the Newborn of a Diabetic Mother: Risk Factors in Prediction and Management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3386;Issue;1;Article No.;20099; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0323698" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0323698</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035985524?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035985524?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jain A., CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Shoran P., CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Vidushi, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India Description An account of the resource Among the metabolic complications, neonatal hypoglycemia remains the most common complication, especially among infants of diabetic mothers. This paper is to briefly review landmark research studies aimed at assessing the prediction of neonatal hypoglycemia with consideration of maternal glucose monitoring, cord blood C-peptide, and HbA1c biomarkers together with advanced AI-based models. This becomes significant in reviewing the pathophysiology of neonatal hypoglycemia, clinical risk factors, and treatment strategies in view of the predictive value of both the mother and the newborn. Maternal diabetes type, gestational age, and neonatal BMI are some of the risk factors to be evaluated. The rapidly growing role of AI in clinical practice would also be discussed. In addition, an individualized management approach might be advocated to improve the outcome of the newborn by employing appropriate clinical tools coupled with AI-based predictive models. 2026 American Institute of Physics Inc.. All rights reserved. Subject The topic of the resource AI prediction; C-peptide; continuous glucose monitoring; diabetic mothers; HbA1c; Neonatal hypoglycemia; risk factors Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online