AI-driven clinical decision support for early diagnosis and treatment planning in patients with suspected sleep apnea using clinical and demographic data before sleep studies
ML techniques applied to routine data allow the identification of meaningful clinical clusters in patients with suspected SA. These clusters can guide differential diagnostic testing and personalized treatment strategies. The ICDSS enables early and accurate patient classification, supporting a precision medicine approach in sleep medicine.
Authors
- María Torres‐Durán (ORCID: https://orcid.org/0000-0001-6710-3766)
- Manuel Casal-Guisande (ORCID: https://orcid.org/0000-0003-1494-8145)
- Mar Mosteiro (ORCID: https://orcid.org/0000-0003-0305-7179)
- Alberto Fernández‐Villar (ORCID: https://orcid.org/0000-0001-7407-5249)
Institutions
- University Hospital Complex Of Vigo (ES)
- Galicia Sur Biomedical Foundation (ES)
- Centro de Investigación Biomédica en Red (ES)
- Centro de Investigación Biomédica en Red de Cáncer (ES)
- Universidade de Vigo (ES)
Publication Details
- Journal
- npj Primary Care Respiratory Medicine
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1038/s41533-025-00455-5
- Citations
- 1
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- European Commission
- Instituto de Salud Carlos III