{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T13:30:31Z","timestamp":1791811831002,"version":"4.3.4"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study evaluates the survival impact of diabetes on hospitalized COVID-19 patients in Mexico by combining traditional survival methods (Restricted Mean Survival Time, RMST) with machine learning (ML) prediction. The goal is to understand how diabetes and associated comorbidities affect short-term survival and to develop accurate, interpretable models that support data-driven decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A national dataset of over one million COVID-19 cases was analyzed. Diabetic and non-diabetic cohorts were matched using propensity scores based on key covariates (e.g., age, gender, and comorbidities). RMST differences were estimated using survival curves and statistical testing. Separately, machine learning models (Random Forest (RF) and Variational Deep Neural Network (VDNN)) were trained to predict individual RMST values, and SHapley Additive exPlanations (SHAP) were used for model interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The RMST for diabetic patients was lower than that for non-diabetic patients, with a difference of 2.32 days (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.0583) after matching. Predictive models achieved strong internal validity (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      &amp;gt; 0.60). SHAP analysis revealed obesity, smoking, and hypertension as the top predictors and suggested that temporal variables and comorbidities played a central role in short-term survival.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Combining survival analysis with machine learning provides both inferential and predictive insights into the mortality risk of diabetic COVID-19 patients. More importantly, results show that traditional survival analyzes with modern machine learning yields accurate and interpretable predictions that can support personalized interventions tailored to patients with COVID-19 and comorbid diabetes: such as prioritizing early clinical monitoring, individualized treatment plans, or risk-informed hospital admission decisions, and guide a more efficient allocation of healthcare resources.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1725251","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T05:43:10Z","timestamp":1768196590000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing survival prediction for COVID-19 in diabetic patients in Mexico: integrating RMST, propensity score matching, and ensemble machine learning"],"prefix":"10.3389","volume":"16","author":[{"given":"Mariano","family":"Vargas-Santiago","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana A.","family":"Le\u00f3n-Velasco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ra\u00fal","family":"Monroy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio","family":"Quezada-Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1209343","article-title":"Covid-19 over the last 3 years in China, what we\u2019ve learned","volume":"11","author":"Shi","year":"2023","journal-title":"Front Public Health"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1002\/jmv.28366","article-title":"Molecular evolution of sars-cov-2 from december 2019 to august 2022","volume":"95","author":"Wolf","year":"2023","journal-title":"J Med Virol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.3390\/pathogens12081047","article-title":"Timeline of sars-cov-2 transmission in sabah, Malaysia: Tracking the molecular evolution","volume":"12","author":"Balakrishnan","year":"2023","journal-title":"Pathogens"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1093\/fampra\/cmab167","article-title":"Outpatient prescription patterns of covid-19 drugs in the metropolitan area of Mexico city","volume":"39","author":"Fuentes-Gonzalez","year":"2022","journal-title":"Fam. 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