ArticleCardiovascular diabetology2025
Development and evaluation of a machine learning prediction model for short-term mortality in patients with diabetes or hyperglycemia at emergency department admission.
Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health.International journal of medical informatics · 2026Article
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11 authors.
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Abstract
backgroundPatients with diabetes admitted to emergency care face a higher risk of complications, including prolonged hospital stays, admissions to the intensive care unit and mortality.
aimTo develop a machine learning (ML) model to predict 30-day mortality in patients with diabetes admitted to the emergency department (ED). DESIGN AND
settingA cohort study utilizing data from all nine ED's in Region Skåne 2017 to 2018. Totally 74,611 patient visits, representing 34,280 unique patients aged > 18 years with diabetes or hyperglycemia (glucose were > 11 mmol/L). The analysis focused on four groups, men and women aged 40-69 and ≥ 70 years.
methodsStochastic gradient boosting was employed to develop a model predicting 30-day mortality. Variable importance was assessed using normalized relative influence (NRI) scores. Variables in certain hospitals were used to train the models, and the models were tested in other hospitals.
resultsKey predictors included laboratory values (pH, base excess, pCO
conclusionsA machine learning model based on routinely collected data in the ED accurately predicted 30-day mortality with high specificity and sensitivity. This approach shows promise in identifying high-risk patients requiring close monitoring and timely interventions.
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