The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review
On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.
Authors
- Lucija Gosak (ORCID: https://orcid.org/0000-0002-8742-6594)
- Gregor Štiglic (ORCID: https://orcid.org/0000-0002-0183-8679)
- Mateja Lorber (ORCID: https://orcid.org/0000-0001-7200-5204)
- Zalika Klemenc–Ketiš (ORCID: https://orcid.org/0000-0002-0270-1754)
- Katrien Verbert (ORCID: https://orcid.org/0000-0001-6699-7710)
- Adrijana Svenšek (ORCID: https://orcid.org/0000-0003-0323-1720)
Institutions
- University of Ljubljana (SI)
- University of Maribor (SI)
- Community Health Centre Ljubljana (SI)
- University of Edinburgh (GB)
- KU Leuven (BE)
Publication Details
- Journal
- JMIR Public Health and Surveillance
- Published
- 2024-10-14
- DOI
- https://doi.org/10.2196/60128
- Citations
- 6
- Primary Topic
- Cardiovascular Health and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 2.41
Funders
- Javna Agencija za Raziskovalno Dejavnost RS