A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.
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| Title: | A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes. |
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| Authors: | Flok A; Accounting and Information Systems, Osnabrück University, Katharinenstr. 1, 49074, Osnabrück, Germany. aleksandra.flok@uni-osnabrueck.de., Kajüter Rodrigues P; Accounting and Information Systems, Osnabrück University, Katharinenstr. 1, 49074, Osnabrück, Germany., Pohurskyy S; Accounting and Information Systems, Osnabrück University, Katharinenstr. 1, 49074, Osnabrück, Germany., Teuteberg F; Accounting and Information Systems, Osnabrück University, Katharinenstr. 1, 49074, Osnabrück, Germany. |
| Source: | BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2026 Jun 03; Vol. 26 (1). Date of Electronic Publication: 2026 Jun 03. |
| Publication Type: | Journal Article; Systematic Review |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1472-6947 |
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| DOI: | 10.1186/s12911-026-03560-8 |
