{"title":"A Machine Learning Framework to Quantify Postprandial Glucose Responses in Gestational Diabetes.","authors":"Souptik Barua, Tenzin Sangmo, Dhairya Upadhyay, Esra Mucahit, Vanessa Martinez, Lauren Berube, Christina Penfield, Hye Heo, Ling-Jun Li, Shauna Williams, Todd Rosen, Shristi Rawal","doi":"10.1089/dtom.2024.0003","DOIUrl":"10.1089/dtom.2024.0003","url":null,"abstract":"<p><strong>Objective: </strong>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).</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>21 participants provided analyzable data. Relative to self-reported mealtime, the ML algorithm's predicted mealtimes had an absolute error of a median 30 [IQR: 20,45] minutes. Comparing 1-hour and 2-hour PPGR values from the CGM using self-reported and ML-predicted mealtimes showed a median difference of 8.7 [IQR: 0,22.7] mg/dL and 3.3 [IQR: 0,13.2] mg/dL respectively for the two timepoints.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":523864,"journal":{"name":"Diabetes technology and obesity medicine","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13192376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148020561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}