A Machine Learning Tool for Identifying Newly Diagnosed Heart Failure in Individuals with Known Diabetes in Primary Care
An SGB model using routinely collected data about diagnoses and number of visits in primary care, can accurately predict risk for diagnosis of heart failure in individuals with diabetes. Age and sex difference in predictive factors warrant further examination.
Authors
- Caroline Wachtler (ORCID: https://orcid.org/0000-0002-1317-7732)
- Per Wändell (ORCID: https://orcid.org/0000-0001-5169-2965)
- Axel Carl Carlsson (ORCID: https://orcid.org/0000-0001-6113-0472)
- Julia Eriksson (ORCID: https://orcid.org/0000-0001-5649-5792)
- Toralph Ruge (ORCID: https://orcid.org/0000-0002-1170-5183)
Institutions
- Malmö University (SE)
- Lund University (SE)
- Karolinska Institutet (SE)
- Primary Health Care (QA)
- Stockholm Health Care Services (SE)
- Skåne University Hospital (SE)
Publication Details
- Journal
- ESC Heart Failure
- Published
- 2024-10-20
- DOI
- https://doi.org/10.1002/ehf2.15115
- Citations
- 6
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 2.41