Machine learning-enabled systematic review on coded healthcare data in heart failure research
One-fifth of contemporary heart failure research articles are already reporting the use of coded healthcare data, with at-scale evaluation facilitated by a machine-learning model. The limited transparency on how coded healthcare data were used in studies highlights the need for quality standards such as the CODE-EHR framework for the use of healthcare data in research.
Authors
- Dipak Kotecha (ORCID: https://orcid.org/0000-0002-2570-9812)
- Diederick E. Grobbee (ORCID: https://orcid.org/0000-0003-4472-4468)
- Megan Schroeder (ORCID: https://orcid.org/0000-0003-4937-2887)
- Folkert W. Asselbergs (ORCID: https://orcid.org/0000-0002-1692-8669)
- Benoît Tyl (ORCID: https://orcid.org/0000-0001-5297-8412)
- Luke T. Slater (ORCID: https://orcid.org/0000-0001-9227-0670)
- Guillaume Allée
- Tomasz Dyszynski
- Alfonso Sartorius
- Simrat Gill (ORCID: https://orcid.org/0000-0002-8302-2891)
- R Thomas Lumbers (ORCID: https://orcid.org/0000-0002-9077-4741)
- Kiliana Suzart‐Woischnik
- Georgios V. Gkoutos (ORCID: https://orcid.org/0000-0002-2061-091X)
Institutions
- Birmingham City University (GB)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- Bayer (United States) (US)
- Utrecht University (NL)
- University Medical Center Utrecht (NL)
- University of Alabama at Birmingham (US)
- National Institute for Health and Care Research (GB)
- Boehringer Ingelheim (Egypt) (EG)
- Servier (Spain) (ES)
- Servier (France) (FR)
- Amsterdam University Medical Centers (NL)
- NIHR Birmingham Biomedical Research Centre (GB)
- University College London (GB)
- Bayer (Germany) (DE)
- University of Birmingham (GB)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1093/ehjdh/ztaf123
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00