Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine
A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.
Authors
- Carsten Eickhoff (ORCID: https://orcid.org/0000-0001-9895-4061)
- Volkmar Falk (ORCID: https://orcid.org/0000-0002-7911-8620)
- Gerhard Hindricks (ORCID: https://orcid.org/0000-0002-0132-0773)
- Sebastian Spethmann (ORCID: https://orcid.org/0000-0003-0987-8007)
- Felix Balzer (ORCID: https://orcid.org/0000-0003-1575-2056)
- Nils Hinrichs (ORCID: https://orcid.org/0000-0002-1989-6156)
- Meike Hiddemann (ORCID: https://orcid.org/0000-0002-2980-5713)
- Nikolaos Dagres (ORCID: https://orcid.org/0000-0003-0323-4956)
- Alexander Meyer (ORCID: https://orcid.org/0000-0002-6944-2478)
- Friedrich Koehler (ORCID: https://orcid.org/0000-0003-0441-4123)
- Thomas Kaas
- Kerstin Koehler
Institutions
- Board of the Swiss Federal Institutes of Technology (CH)
- Deutsches Herzzentrum der Charité (DE)
- German Centre for Cardiovascular Research (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Technische Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
- University of Tübingen (DE)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2024-09-20
- DOI
- https://doi.org/10.3389/fcvm.2024.1457995
- Citations
- 9
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 3.62
Funders
- Bundesministerium für Bildung und Forschung