{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:03:22Z","timestamp":1790100202098,"version":"4.0.1"},"reference-count":48,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to systematically evaluate the predictive performance of machine learning (ML)\u2013based models for predicting hypoglycemia in Chinese patients with diabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, CNKI, and Wanfang databases from inception to February 2026. Eligible studies focused on the development or validation of ML\u2013based models for predicting hypoglycemia in Chinese patients with diabetes. Study selection and data extraction were performed independently by two reviewers. Information on study characteristics, modeling approaches, predictors, validation methods, and model performance was collected. The area under the receiver operating characteristic curve (AUC) was synthesized using a random-effects model. Study quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 13 studies were included, and the pooled prevalence of hypoglycemia was 25% (95% CI: 17%\u201333%). The overall pooled area under the receiver operating characteristic curve (AUC) was 0.90 (95% CI: 0.87\u20130.93). Subgroup analyses by modeling algorithms showed pooled AUCs of 0.89 for extreme gradient boosting (XGBoost), 0.88 for random forest (RF), 0.85 for support vector machine (SVM), 0.84 for Light Gradient Boosting Machine (LightGBM), 0.83 for logistic regression (LR), and 0.81 for decision tree (DT) models. Common predictors included age, insulin use, body mass index, HbA1c, creatinine, and history of hypoglycemia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We attempted to provide a comprehensive overview of machine learning\u2013based prediction models for hypoglycemia in patients with diabetes. Research in this field remains at an early stage, although several models with good discriminatory performance have been reported. Methodological limitations and insufficient validation were observed in many studies. Concerns regarding model robustness and interpretability also exist. More efforts to develop reliable and interpretable models and to promote their application in clinical practice for early risk identification are needed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0358876","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:23:31Z","timestamp":1790097811000},"page":"e0358876","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance of machine learning\u2013based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis"],"prefix":"10.1371","volume":"21","author":[{"given":"Jinhua","family":"Yan","sequence":"first","affiliation":[{"name":"the People\u2019s Hospital of Leshan","place":["Leshan, China"],"department":["Department of General 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Health"},{"issue":"3","key":"pone.0358876.ref004","doi-asserted-by":"crossref","first-page":"100215","DOI":"10.1016\/j.focus.2024.100215","article-title":"Prevalence of Diabetes by BMI: China Nutrition and Health Surveillance (2015-2017) and U.S. National Health and Nutrition Examination Survey (2015-2018)","volume":"3","author":"D Yu","year":"2024","journal-title":"AJPM Focus"},{"issue":"2","key":"pone.0358876.ref005","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1111\/1753-0407.12165","article-title":"China type 2 diabetes treatment status survey of treatment pattern of oral drugs users","volume":"7","author":"L Ji","year":"2015","journal-title":"J Diabetes"},{"issue":"5","key":"pone.0358876.ref006","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1007\/s00125-020-05366-3","article-title":"The consequences of hypoglycaemia","volume":"64","author":"SA Amiel","year":"2021","journal-title":"Diabetologia"},{"key":"pone.0358876.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gerinurse.2025.02.015","article-title":"Prevalence and factors of fear of hypoglycemia among Chinese older adults with type 2 diabetes mellitus: A cross-sectional study","volume":"63","author":"Q Tang","year":"2025","journal-title":"Geriatr Nurs"},{"key":"pone.0358876.ref008","article-title":"The Role of Hypoglycemia in Cardiovascular Outcomes in Diabetes","author":"BW Paty","year":"2015","journal-title":"Can J Diabetes"},{"issue":"11","key":"pone.0358876.ref009","doi-asserted-by":"crossref","first-page":"9357","DOI":"10.3390\/ijms24119357","article-title":"Effects of Hypoglycemia on Cardiovascular Function in Patients with Diabetes","volume":"24","author":"MA Christou","year":"2023","journal-title":"Int J Mol Sci"},{"issue":"5","key":"pone.0358876.ref010","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S2213-8587(18)30315-2","article-title":"Hypoglycaemia, cardiovascular disease, and mortality in diabetes: epidemiology, pathogenesis, and management","volume":"7","author":"Group International Hypoglycaemia Study","year":"2019","journal-title":"Lancet Diabetes Endocrinol"},{"issue":"4","key":"pone.0358876.ref011","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1111\/dme.13897","article-title":"Importance of inpatient hypoglycaemia: impact, prediction and prevention","volume":"36","author":"Y Ruan","year":"2019","journal-title":"Diabet Med"},{"issue":"1","key":"pone.0358876.ref012","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1097\/ALN.0000000000004764","article-title":"Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools","volume":"140","author":"P Arina","year":"2024","journal-title":"Anesthesiology"},{"issue":"1","key":"pone.0358876.ref013","doi-asserted-by":"crossref","first-page":"2388171","DOI":"10.1080\/14767058.2024.2388171","article-title":"Preeclampsia and its prediction: traditional versus contemporary predictive methods","volume":"37","author":"W Feng","year":"2024","journal-title":"J Matern Fetal Neonatal Med"},{"issue":"1","key":"pone.0358876.ref014","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1186\/s12933-023-01985-3","article-title":"Machine learning in precision diabetes care and cardiovascular risk prediction","volume":"22","author":"EK Oikonomou","year":"2023","journal-title":"Cardiovasc Diabetol"},{"issue":"8","key":"pone.0358876.ref015","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.amjmed.2020.03.033","article-title":"Artificial Intelligence: The Future for Diabetes Care","volume":"133","author":"S Ellahham","year":"2020","journal-title":"Am J Med"},{"issue":"12","key":"pone.0358876.ref016","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11892-021-01423-2","article-title":"Artificial Intelligence in Current Diabetes Management and Prediction","volume":"21","author":"A Nomura","year":"2021","journal-title":"Curr Diab Rep"},{"key":"pone.0358876.ref017","article-title":"Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis","volume":"11","author":"K Liu","year":"2023","journal-title":"JMIR Med Inform"},{"issue":"4","key":"pone.0358876.ref018","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1001\/jama.2017.19163","article-title":"Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement","volume":"319","author":"and the PRISMA-DTA Group","year":"2018","journal-title":"JAMA"},{"issue":"5","key":"pone.0358876.ref019","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1111\/wvn.12598","article-title":"A guided search: Formulating a PICOT from assigned areas of inquiry","volume":"19","author":"TD McClinton","year":"2022","journal-title":"Worldviews Evid Based Nurs"},{"issue":"10","key":"pone.0358876.ref020","article-title":"Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist","volume":"11","author":"KGM Moons","year":"2014","journal-title":"PLoS Med"},{"issue":"1","key":"pone.0358876.ref021","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies","volume":"170","author":"RF Wolff","year":"2019","journal-title":"Ann Intern Med"},{"issue":"12","key":"pone.0358876.ref022","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1111\/nep.13913","article-title":"Appraising prediction research: a guide and meta-review on bias and applicability assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)","volume":"26","author":"Y de Jong","year":"2021","journal-title":"Nephrology (Carlton)"},{"key":"pone.0358876.ref023","article-title":"A guide to systematic review and meta-analysis of prediction model performance","volume":"356","author":"TPA Debray","year":"2017","journal-title":"BMJ"},{"issue":"7109","key":"pone.0358876.ref024","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1136\/bmj.315.7109.629","article-title":"Bias in meta-analysis detected by a simple, graphical test","volume":"315","author":"M Egger","year":"1997","journal-title":"BMJ"},{"issue":"39","key":"pone.0358876.ref025","first-page":"1829","article-title":"Construction of a hypoglycemia prediction model for older adults with type 2 diabetes based on random forest algorithm","volume":"23","author":"Z Ruiting","year":"2023","journal-title":"Chinese J Pract Nurs"},{"key":"pone.0358876.ref026","unstructured":"Zheng C. Construction of a hypoglycemia prediction model based on a machine learning algorithm for hospitalized type 2 diabetes patients: Anhui University of Chinese Medicine. 2023."},{"key":"pone.0358876.ref027","unstructured":"Niu W. Machine Learning-based Model For Predicting Hypoglycemic Events During Hospitalization in Patients with T2DM: Henan University of Science and Technology."},{"key":"pone.0358876.ref028","unstructured":"R. Z. Research and application of machine learning-based models for diabetes diagnosis and blood glucose prediction: Southwest Petroleum University. 2023."},{"key":"pone.0358876.ref029","unstructured":"Yi M. Development and validation of a hypoglycemia risk prediction model in elderly patients with type 2 diabetes mellitus and cognitive impairment: Soochow University. 2024."},{"issue":"6","key":"pone.0358876.ref030","doi-asserted-by":"crossref","DOI":"10.2196\/36958","article-title":"Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record-Based Machine Learning: Development and Validation","volume":"10","author":"H Yang","year":"2022","journal-title":"JMIR Med Inform"},{"issue":"10","key":"pone.0358876.ref031","doi-asserted-by":"crossref","DOI":"10.1002\/nop2.70055","article-title":"Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus","volume":"11","author":"C Gong","year":"2024","journal-title":"Nurs Open"},{"issue":"4","key":"pone.0358876.ref032","article-title":"A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study","volume":"21","author":"M Shi","year":"2024","journal-title":"PLoS Med"},{"key":"pone.0358876.ref033","doi-asserted-by":"crossref","DOI":"10.2196\/56909","article-title":"Generalization of a Deep Learning Model for Continuous Glucose Monitoring-Based Hypoglycemia Prediction: Algorithm Development and Validation Study","volume":"12","author":"J Shao","year":"2024","journal-title":"JMIR Med Inform"},{"key":"pone.0358876.ref034","doi-asserted-by":"crossref","first-page":"1685969","DOI":"10.3389\/fendo.2025.1685969","article-title":"Machine learning-based online prediction of nocturnal hypoglycemia in elderly patients with type 2 diabetes","volume":"16","author":"Y Liu","year":"2026","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"pone.0358876.ref035","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1186\/s12902-025-02104-x","article-title":"Construction and validation of a hypoglycemia risk 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associated with the incidence of type 2 diabetes mellitus in middle-aged and older populations in China: evidence from a population-based cross-sectional study","volume":"127","author":"X Ma","year":"2024","journal-title":"Nutr Res"},{"issue":"1","key":"pone.0358876.ref039","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1038\/s41598-019-56927-5","article-title":"Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG","volume":"10","author":"M Porumb","year":"2020","journal-title":"Sci Rep-Uk"},{"issue":"1","key":"pone.0358876.ref040","doi-asserted-by":"crossref","first-page":"12808","DOI":"10.1038\/s41598-025-98075-z","article-title":"Comparative study of XGBoost and logistic regression for predicting sarcopenia in postsurgical gastric cancer patients","volume":"15","author":"Y Gu","year":"2025","journal-title":"Sci Rep"},{"key":"pone.0358876.ref041","first-page":"1015","article-title":"Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology","volume":"5","author":"EJ Oh","year":"2021","journal-title":"JCO Clin Cancer Inform"},{"issue":"6","key":"pone.0358876.ref042","doi-asserted-by":"crossref","DOI":"10.1210\/clinem\/dgac034","article-title":"Complications of Diabetes and Metrics of Glycemic Management Derived From Continuous Glucose Monitoring","volume":"107","author":"M Yapanis","year":"2022","journal-title":"J Clin Endocrinol Metab"},{"issue":"8103","key":"pone.0358876.ref043","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1038\/s41586-025-09925-9","article-title":"A foundation model for continuous glucose monitoring data","volume":"650","author":"G Lutsker","year":"2026","journal-title":"Nature"},{"issue":"6","key":"pone.0358876.ref044","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s13300-023-01403-7","article-title":"Classification of Hypoglycemic Events in Type 1 Diabetes Using Machine Learning Algorithms","volume":"14","author":"L Cederblad","year":"2023","journal-title":"Diabetes Ther"},{"key":"pone.0358876.ref045","first-page":"20552076221129712","article-title":"HYPO-CHEAT\u2019s aggregated weekly visualisations of risk reduce real world hypoglycaemia","volume":"8","author":"C Worth","year":"2022","journal-title":"Digit Health"},{"issue":"5","key":"pone.0358876.ref046","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1177\/19322968241236208","article-title":"A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data","volume":"19","author":"F Giammarino","year":"2025","journal-title":"J Diabetes Sci Technol"},{"issue":"6","key":"pone.0358876.ref047","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.pcd.2025.09.006","article-title":"Predicting the future risk of developing type 2 diabetes in women with a history of gestational diabetes mellitus using machine learning and explainable 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