{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T23:11:19Z","timestamp":1787008279772,"version":"build-2736575974"},"reference-count":17,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Technology and Obesity Medicine"],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To develop a machine learning (ML) framework to identify postprandial glucose responses (PPGR) automatically from continuous glucose monitoring (CGM) data in pregnant adults with gestational diabetes mellitus (GDM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Pregnant adults diagnosed with GDM or impaired glucose tolerance (IGT) wore blinded CGMs and logged mealtimes for up to three 14-day time periods after enrollment. A random forest ML algorithm was applied to identify morning PPGRs from daily CGM profiles, and its performance compared against PPGRs derived using self-reported mealtimes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Twenty-one participants provided analyzable data. Relative to self-reported mealtime, the ML algorithm\u2019s predicted mealtimes had an absolute error of a median 30 (interquartile range [IQR]: 20\u201345) min. Comparing 1-h and 2-h PPGR values from the CGM using self-reported and ML-predicted mealtimes showed a median difference of 8.7 (IQR: 0\u201322.7) mg\/dL and 3.3 (IQR: 0\u201313.2) mg\/dL, respectively, for the two timepoints.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>A random forest ML algorithm accurately identified PPGRs from CGM data in persons with GDM, enabling an automated and convenient approach to monitoring postprandial dysglycemia in this population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/dtom.2024.0003","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T17:01:07Z","timestamp":1742230867000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Machine Learning Framework to Quantify Postprandial Glucose Responses in Gestational Diabetes"],"prefix":"10.1177","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1675-8874","authenticated-orcid":false,"given":"Souptik","family":"Barua","sequence":"first","affiliation":[{"name":"Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tenzin","family":"Sangmo","sequence":"additional","affiliation":[{"name":"Department of Clinical and Preventive Nutrition Sciences, School of Health Professions, Rutgers University, Newark, New Jersey, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhairya","family":"Upadhyay","sequence":"additional","affiliation":[{"name":"Division of Precision Medicine, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esra","family":"Mucahit","sequence":"additional","affiliation":[{"name":"Department of Clinical and Preventive Nutrition Sciences, School of Health Professions, Rutgers University, Newark, New Jersey, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Martinez","sequence":"additional","affiliation":[{"name":"Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, and Reproductive Medicine, Rutgers University-Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren","family":"Berube","sequence":"additional","affiliation":[{"name":"Public Health Nutrition Program, Department of Epidemiology, NYU School of Global Public Health, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina A.","family":"Penfield","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, NYU Grossman School of Medicine, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, NYU Grossman Long Island School of Medicine, New York, New York, USA."},{"name":"Department of Health Informatics, NYU Grossman School of Medicine, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Jun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Obstetrics &amp; Gynaecology, Yong Loo Lin School of Medicine, NUS Singapore, Singapore."},{"name":"Global Centre for Asian Women's Health (GloW), Yong Loo Lin School of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shauna","family":"Williams","sequence":"additional","affiliation":[{"name":"Department of Obstetrics, Gynecology and Reproductive Health, Rutgers New Jersey Medical School, Newark, New Jersey, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Todd","family":"Rosen","sequence":"additional","affiliation":[{"name":"Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, and Reproductive Medicine, Rutgers University-Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shristi","family":"Rawal","sequence":"additional","affiliation":[{"name":"Department of Clinical and Preventive Nutrition Sciences, School of Health Professions, Rutgers University, Newark, New Jersey, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/AOG.0000000000002501"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajogmf.2019.100031"},{"key":"e_1_3_5_4_2","first-page":"193229682412748","article-title":"Estimating breakfast characteristics using continuous glucose monitoring and machine learning in adults with or at risk of type 2 diabetes","author":"Pai R","year":"2024","unstructured":"Pai R, , Barua S, , Kim BS, et al. 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