Development and evaluation of a machine learning prediction model for short-term mortality in patients with diabetes or hyperglycemia at emergency department admission
A 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.
Authors
- Caroline Wachtler (ORCID: https://orcid.org/0000-0002-1317-7732)
- Anders Björkelund (ORCID: https://orcid.org/0000-0001-7426-914X)
- Per Wändell (ORCID: https://orcid.org/0000-0001-5169-2965)
- Ulf Ekelund (ORCID: https://orcid.org/0000-0002-0951-1582)
- Torgny Wessman (ORCID: https://orcid.org/0000-0002-7314-2240)
- Olle Melander (ORCID: https://orcid.org/0000-0002-2581-484X)
- Toralph Ruge (ORCID: https://orcid.org/0000-0002-1170-5183)
- Anna Olofsson (ORCID: https://orcid.org/0000-0002-7479-315X)
- Axel C. Carlsson
- Marcelina Wierzbicka
- Karolina Sigurdsson
Institutions
- Dalarna University (SE)
- Lund University (SE)
- Karolinska Institutet (SE)
- Stockholm Health Care Services (SE)
- Skåne University Hospital (SE)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1186/s12933-025-02954-8
- Primary Topic
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Type
- article
- Field-Weighted Citation Impact
- 0.00