A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy
Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments.
Authors
- Daniele Spoladore (ORCID: https://orcid.org/0000-0002-0527-070X)
- Cláudio Bettini (ORCID: https://orcid.org/0000-0002-1727-7650)
- Martina Tosi (ORCID: https://orcid.org/0000-0003-4859-5288)
- Erna Cecilia Lorenzini (ORCID: https://orcid.org/0000-0003-4344-4130)
- Francesco Stella
Institutions
- University of Milan (IT)
- National Research Council (IT)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-08-09
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109001
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 3.54
Funders
- Musashino University
- Center for Risk-Based Community Resilience Planning