{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T21:06:03Z","timestamp":1780088763063,"version":"3.54.0"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.bspc.2026.110398","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T12:18:27Z","timestamp":1778761107000},"page":"110398","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["Gestational diabetes prediction and diet recommendation using lattice homomorphism-based deep neural network"],"prefix":"10.1016","volume":"123","author":[{"given":"Anjali","family":"Jain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alka","family":"Singhal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110398_b0005","doi-asserted-by":"crossref","unstructured":"Benham, Jamie L., Veronique Gingras, Niamh-Maire McLennan, Jasper Most, Jennifer M. Yamamoto, Catherine E. Aiken, Susan E. Ozanne, Rebecca M. Reynolds, and ADA\/EASD PMDI. \u201cPrecision Gestational Diabetes Treatment: Systematic review and Meta-analysis.\u201dmedRxiv(2023): 2023-04.","DOI":"10.1101\/2023.04.15.23288459"},{"issue":"3","key":"10.1016\/j.bspc.2026.110398_b0010","doi-asserted-by":"crossref","first-page":"487","DOI":"10.3390\/nu15030487","article-title":"Supports and barriers to lifestyle interventions in women with gestational diabetes mellitus in Australia: a National Online Survey","volume":"15","author":"Sabag","year":"2023","journal-title":"Nutrients"},{"key":"10.1016\/j.bspc.2026.110398_b0015","doi-asserted-by":"crossref","unstructured":"Benham, Jamie L., Veronique Gingras, Niamh-Maire McLennan, Jasper Most, Jennifer M. Yamamoto, Catherine E. Aiken, Susan E. Ozanne, Rebecca M. Reynolds, and ADA\/EASD PMDI. \u201cPrecision Gestational Diabetes Treatment: Systematic review and Meta-analysis.\u201dmedRxiv(2023): 2023-04.","DOI":"10.1101\/2023.04.15.23288459"},{"issue":"1","key":"10.1016\/j.bspc.2026.110398_b0020","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1002\/fsn3.3042","article-title":"Dose-response association between dietary patterns and gestational diabetes mellitus risk: a systematic review and meta\u2010analysis of observational studies","volume":"11","author":"Haghighatdoost","year":"2023","journal-title":"Food Sci. Nutr."},{"issue":"7","key":"10.1016\/j.bspc.2026.110398_b0025","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.3390\/nu15071613","article-title":"The Association of Specific Dietary Patterns with Cardiometabolic Outcomes in Women with a history of Gestational Diabetes Mellitus: a Scoping Review","volume":"15","author":"O\u2019Hara","year":"2023","journal-title":"Nutrients"},{"key":"10.1016\/j.bspc.2026.110398_b0030","doi-asserted-by":"crossref","unstructured":"Zakaria, Hala, Salah Abusanana, Bashair M. Mussa, Ayesha S. Al Dhaheri, Lily Stojanovska, Maysm N. Mohamad, Sheima T. Saleh, Habiba I. Ali, and Leila Cheikh Is\u201cThe Role of Lifestyle Interventions in the Prevention and Treatment of Gestational Diabetes Mellitus.\u201dMedicina59, no. 2 (2023): 287.","DOI":"10.3390\/medicina59020287"},{"issue":"11","key":"10.1016\/j.bspc.2026.110398_b0035","doi-asserted-by":"crossref","first-page":"E396","DOI":"10.1503\/cmaj.221404","article-title":"The effect of changing screening practices and demographics on the incidence of gestational diabetes in British Columbia, 2005\u20132019","volume":"195","author":"Nethery","year":"2023","journal-title":"CMAJ"},{"issue":"3","key":"10.1016\/j.bspc.2026.110398_b0040","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2022-065335","volume":"13","author":"Boath","year":"2023","journal-title":"BMJ Open"},{"key":"10.1016\/j.bspc.2026.110398_b0045","doi-asserted-by":"crossref","DOI":"10.1111\/jhn.13191","article-title":"Priorities to improve woman-centered gestational diabetes mellitus care: a qualitative study to compare views between clinical and consumer end\u2010users","author":"Wan","year":"2023","journal-title":"J. Hum. Nutr. Diet."},{"issue":"1","key":"10.1016\/j.bspc.2026.110398_b0050","doi-asserted-by":"crossref","DOI":"10.1080\/14767058.2022.2155043","article-title":"Supervised physical activity and the incidence of gestational diabetes mellitus: a systematic review and meta-analysis","volume":"36","author":"Bennett","year":"2023","journal-title":"J. Matern. Fetal Neonatal Med."},{"key":"10.1016\/j.bspc.2026.110398_b0055","doi-asserted-by":"crossref","unstructured":"Nikparast, Ali, Jamal Rahmani, Reza Bagheri, Saba Mohammadpour, Mehdi Shadnoosh, Alexei Wong, and Matin Ghanavati. \u201cMaternal uric acid levels and risk of gestational diabetes mellitus: A systematic review and dose\u2013response meta\u2010analysis of cohort studies including 105,380 participants.\u201dJournal of Diabetes Investigation(2023).","DOI":"10.1111\/jdi.14022"},{"key":"10.1016\/j.bspc.2026.110398_b0060","doi-asserted-by":"crossref","unstructured":"Phelan, Suzanne, Elissa Jelalian, Donald Coustan, Aaron B. Caughey, Kristin Castorino, Todd Hagobian, Karen Mu\u00f1oz-Christian et al. \u201cRandomized controlled trial of prepregnancy lifestyle intervention to reduce the recurrence of gestational diabetes mellitus.\u201d American journal of obstetrics and gynecology(2023).","DOI":"10.1016\/j.ajog.2023.01.037"},{"issue":"1","key":"10.1016\/j.bspc.2026.110398_b0065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40001-023-01088-5","article-title":"Gedefaye Nibret Mihretie, and Alemu Degu Ayele. \u201cGestational diabetes mellitus and its associated factors in Ethiopia: a systematic review and meta-analysis.\u201d","volume":"28","author":"Beyene","year":"2023","journal-title":"Eur. J. Med. Res."},{"key":"10.1016\/j.bspc.2026.110398_b0070","volume":"13, no. 3","author":"Boath","year":"2023","journal-title":"BMJ Open"},{"key":"10.1016\/j.bspc.2026.110398_b0075","doi-asserted-by":"crossref","DOI":"10.3389\/fendo.2023.1119134","article-title":"Predictors for pharmacological therapy and perinatal outcomes with metformin treatment in women with gestational diabetes","volume":"14","author":"Brzozowska","year":"2023","journal-title":"Front. Endocrinol."},{"key":"10.1016\/j.bspc.2026.110398_b0080","series-title":"Complex Clinical Data and Gestational Diabetes Mellitus","year":"2023"},{"key":"10.1016\/j.bspc.2026.110398_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.hsr.2023.100086","article-title":"Pharmaco-epi-genetic and Patho-physiology of Gestational Diabetes Mellitus (GDM): an Overview","author":"Shamsad","year":"2023","journal-title":"Health Sciences Review"},{"issue":"7","key":"10.1016\/j.bspc.2026.110398_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdiacomp.2023.108513","article-title":"Prediction of pre-diabetes and type 2 diabetes nine years postpartum using serum metabolome in pregnant women with gestational diabetes requiring pharmacological treatment","volume":"37","author":"Huhtala","year":"2023","journal-title":"J. Diabetes Complications"},{"key":"10.1016\/j.bspc.2026.110398_b0095","doi-asserted-by":"crossref","unstructured":"Kragelund Nielsen, Karoline, Emma Davidsen, Anne Husted Henriksen, and Gregers S. Andersen. \u201cGestational diabetes and international migration.\u201dJournal of the Endocrine Society7, no. 1 (2023): bvac160.","DOI":"10.1210\/jendso\/bvac160"},{"issue":"2","key":"10.1016\/j.bspc.2026.110398_b0100","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1111\/dom.14886","article-title":"Impact of iron supplementation on gestational diabetes mellitus: a literature review","volume":"25","author":"Liu","year":"2023","journal-title":"Diabetes. Obes. Metab."},{"key":"10.1016\/j.bspc.2026.110398_b0145","doi-asserted-by":"crossref","unstructured":"Jain, A., Singhal, A. (2023). Early Diabetes Prediction Using Deep Ensemble Model and Diet Planning. In: Devedzic, V., Agarwal, B., Gupta, M.K. (eds) Proceedings of the International Conference on Intelligent Computing, Communication and Information Security. ICICCIS 2022. Algorithms for Intelligent Systems. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-1373-2_8.","DOI":"10.1007\/978-981-99-1373-2_8"},{"key":"10.1016\/j.bspc.2026.110398_b0150","doi-asserted-by":"crossref","unstructured":"A. Jain and A. Singhal, \u201cDiet Recommendation using Predictive Learning Approaches,\u201d 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, 2022, pp. 1-5, doi: 10.1109\/ICICT55121.2022.10064538.","DOI":"10.1109\/ICICT55121.2022.10064538"},{"key":"10.1016\/j.bspc.2026.110398_b0155","doi-asserted-by":"crossref","unstructured":"A. Jain and A. Singhal, \u201cUtilizing Metaheuristic Machine Learning Techniques for Early Diabetes Detection,\u201d 2023 Second International Conference on Informatics (ICI), Noida, India, 2023, pp. 1-6, doi: 10.1109\/ICI60088.2023.10421100.","DOI":"10.1109\/ICI60088.2023.10421100"},{"issue":"12","key":"10.1016\/j.bspc.2026.110398_b0160","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.1049\/iet-rpg.2018.5917","article-title":"Data\u2010driven wind speed forecasting using deep feature extraction and LSTM","volume":"13","author":"Wu","year":"2019","journal-title":"IET Renew. Power Gener."},{"key":"10.1016\/j.bspc.2026.110398_b0105","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1007\/s00366-020-00971-7","article-title":"Enhanced a hybrid moth-flame optimization algorithm using new selection schemes","volume":"37","author":"Shehab","year":"2021","journal-title":"Eng. Comput."},{"key":"10.1016\/j.bspc.2026.110398_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2020.107329","article-title":"A deep learning approach based on convolutional LSTM for detecting diabetes","volume":"88","author":"Rahman","year":"2020","journal-title":"Comput. Biol. Chem."},{"key":"10.1016\/j.bspc.2026.110398_b0115","article-title":"GDM Dataset.xlsx. figshare","author":"Jeyaparam","year":"2023","journal-title":"Dataset"},{"key":"10.1016\/j.bspc.2026.110398_b0165","doi-asserted-by":"crossref","unstructured":"Naveena, Somasundaram, and Ayyasamy Bharathi. \u201cWeighted entropy deep features on hybrid RNN with LSTM for glucose level and diabetes prediction.\u201d Computer Methods in Biomechanics and Biomedical Engineering 26, no. 15 (2023): 1834-1858. RNN-LSTM.","DOI":"10.1080\/10255842.2022.2149263"},{"key":"10.1016\/j.bspc.2026.110398_b0170","doi-asserted-by":"crossref","unstructured":"El-Rashidy, Nora, Nesma E. ElSayed, Amir El-Ghamry, and Fatma M. Talaat. \u201cPrediction of gestational diabetes based on explainable deep learning and fog computing.\u201d Soft Computing-A Fusion of Foundations, Methodologies & Applications 26, no. 21 (2022). EPM using DNN.","DOI":"10.1007\/s00500-022-07420-1"},{"issue":"3","key":"10.1016\/j.bspc.2026.110398_b0175","doi-asserted-by":"crossref","first-page":"49","DOI":"10.37190\/abb\/209528","article-title":"A novel data mining approach for early diagnosis of gestational diabetes mellitus (GDM) in pregnancy via machine learning methods and CNN","volume":"27","author":"Ba\u015f\u00e7il","year":"2025","journal-title":"Acta Bioeng. Biomech."},{"key":"10.1016\/j.bspc.2026.110398_b0180","first-page":"6810","article-title":"Early prediction of gestational diabetes using machine learning techniques","volume":"101","author":"Alotaibi","year":"2023","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"10.1016\/j.bspc.2026.110398_b0185","series-title":"Women with a history of gestational diabetes mellitus present an accumulation of cardiovascular risk factors at age 46\u2014a birth cohort study","author":"Bakiris","year":"2024"},{"key":"10.1016\/j.bspc.2026.110398_b0190","doi-asserted-by":"crossref","unstructured":"Hassan, Ahmad, Saima Gulzar Ahmad, Tassawar Iqbal, Ehsan Ullah Munir, Kashif Ayyub, and Naeem Ramzan. \u201cEnhanced model for gestational diabetes mellitus prediction using a fusion technique of multiple algorithms with explainability.\u201d International Journal of Computational Intelligence Systems 18, no. 1 (2025): 47.","DOI":"10.1007\/s44196-025-00760-4"},{"key":"10.1016\/j.bspc.2026.110398_b0120","article-title":"A comparative analysis for diabetic prediction based on machine learning techniques","volume":"47, no. 1","author":"Noori","year":"2021","journal-title":"Journal of Basrah Researches (sciences)"},{"key":"10.1016\/j.bspc.2026.110398_b0125","doi-asserted-by":"crossref","unstructured":"Gao, Weihao, Zhuo Deng, Zheng Gong, Ziyi Jiang, and Lan Ma. \u201cAI-driven Prediction of Insulin Resistance in Normal Populations: Comparing Models and Criteria.\u201darXiv preprint arXiv:2503.05119(2025).","DOI":"10.2139\/ssrn.5216158"},{"key":"10.1016\/j.bspc.2026.110398_b0130","doi-asserted-by":"crossref","unstructured":"Belsti, Yitayeh, Lisa Moran, Lan Du, Aya Mousa, Kushan De Silva, Joanne Enticott, and Helena Teede. \u201cComparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model.\u201dInternational Journal of Medical Informatics179 (2023): 105228.","DOI":"10.1016\/j.ijmedinf.2023.105228"},{"key":"10.1016\/j.bspc.2026.110398_b0135","article-title":"\u201cA Novel Approach for Prediction of Gestational Diabetes based on Clinical signs and Risk Factors.\u201dEAI Endorsed transactions on Scalable","volume":"10, no. 3","author":"Reddy","year":"2023","journal-title":"Inf. Syst."},{"key":"10.1016\/j.bspc.2026.110398_b0140","doi-asserted-by":"crossref","unstructured":"Yang, Meng-Nan, Lin Zhang, Wen-Juan Wang, Rong Huang, Hua He, Tao Zheng, Guang-Hui Zhang et al. \u201cPrediction of gestational diabetes mellitus by multiple biomarkers at early gestation.\u201dBMC Pregnancy and Childbirth24, no. 1 (2024): 601.","DOI":"10.1186\/s12884-024-06651-4"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009523?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009523?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:10:35Z","timestamp":1780085435000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":38,"alternative-id":["S1746809426009523"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110398","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Gestational diabetes prediction and diet recommendation using lattice homomorphism-based deep neural network","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110398","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110398"}}