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 Article Faculty Publications -Articles 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 Chanumolu, Kiran Kumar; G., Muni Nagamani; G., Muni Hemalatha Title A name given to the resource Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction 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 Current Diabetes Reviews;Volume;22;Issue;8; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2174/0115733998380389251111041618" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/0115733998380389251111041618</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037044320?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037044320?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 Chanumolu K.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Deemed to be University, Green Fields, Vaddeswaram, Andhra Pradesh, 522237, India; G. M.N., Department of Computer Science and Engineering, Andhra Loyola Institute of Engineering and Technology, Andhra Pradesh, Vijayawada, 520008, India; G. M.H., Department of Chemistry, Mohan Babu University, Sree Sainath Nagar, Chittoor (District), Andhra Pradesh, Tirupati, 517102, India Description An account of the resource Gestational diabetes mellitus (GDM) affects almost 10%-12% of pregnancies worldwide, threatening maternal and fetal life. Continuous glucose monitoring (CGM) forms the backbone of managing GDM, and the current methodologies largely disregard physiological and behavioral factors, thereby greatly reducing accuracy and clinical interpretability. Methods: A hybrid deep learning framework was developed by fusing CGM with multi-sensing modality data, including heart rate, activity levels, sleep patterns, and dietary intake. For data preprocessing, Kalman filtering was applied for temporal alignment, adaptive normalization provided outlier handling and imputation, while the CNN-BiLSTM backbone with attention was harnessed for feature extraction. A Multi-Task Attention Fusion Network (MTAFN) was used to predict glucose values and classify GDM risk simultaneously, while SHAP and dynamic smoothing contributed to interpretability sets. Results: The framework was validated on an extended OhioT1DM dataset with adaptations for pregnancy. It reached a glucose prediction RMSE of 9.8 mg/dL and a GDM risk classification accuracy of 93%. Compared to competitive approaches, the present solution attained a 25% better accuracy on interpretability and an improvement in sensitivity and specificity of about 4-6% across various physiological conditions. Discussion: The use of multi-sensing data increased prediction robustness by capturing complex physiological dependencies. The SHAP-based interpretability justified the predictions through a physiological lens. With an attention mechanism for feature weighting, it was possible to identify crucial variables like meal intake and nighttime variability in the workflow sets. Conclusion: The hybrid framework proposed here is reliable for clinically interpretable continuous glucose monitoring and GDM risk predictions. Its application with high reliability can lead to integrating it within clinical protocols for real-time maternal care sets. 2026, The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Subject The topic of the resource Continuous glucose monitoring; deep learning; gestational diabetes prediction; interpretive dynamic smoothing; maternal care; multi-sensor data integration; scenarios Publisher An entity responsible for making the resource available Bentham Science Publishers Relation A related resource ISSN: 15733998; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online