Predicting diabetes mellitus metabolic goals and chronic complications transitions—analysis based on natural language processing and machine learning models
Patient compliance with pharmacological treatments, professional adherence to clinical practice guidelines, and lifestyle interventions play a crucial role in diabetes progression. While our models provide strong predictive capabilities, improving data quality and integration remains essential for better forecasting and intervention strategies.
Authors
- Andrés García-Suaza
- Sara Atehortúa
- Esteban Morales-Mendoza (ORCID: https://orcid.org/0009-0001-1189-8288)
- J. M. Castro Rincón
- Javier Enrique Camacho-Cogollo (ORCID: https://orcid.org/0000-0003-0252-4580)
- Claudia Carolina Colmenares Mejía (ORCID: https://orcid.org/0000-0003-1847-4282)
- Paul Rodríguez‐Lesmes (ORCID: https://orcid.org/0000-0003-1058-3062)
- Juan Pablo Martínez (ORCID: https://orcid.org/0000-0002-7503-3339)
- Mario A. Isaza-Ruget (ORCID: https://orcid.org/0000-0003-0243-9263)
- Christian Lochmüller (ORCID: https://orcid.org/0009-0008-7322-5311)
- Yohan R Céspedes
Institutions
- EIA University (CO)
- Universidad de Antioquia (CO)
- Fundación Universitaria Sanitas (CO)
- Universidad del Rosario (CO)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1371/journal.pone.0321258
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 5.88
Funders
- World Bank Group