Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Gugatschka, M; Egger, NM; Haspl, K; Hortobagyi, D; Jauk, S; Feiner, M; Kramer, D.
Clinical evaluation of a machine learning-based dysphagia risk prediction tool.
Eur Arch Otorhinolaryngol. 2024;
Doi: 10.1007/s00405-024-08678-x
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- Führende Autor*innen der Med Uni Graz
- Gugatschka Markus
- Co-Autor*innen der Med Uni Graz
- Hortobagyi David
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- Abstract:
- PURPOSE: The rise of digitization promotes the development of screening and decision support tools. We sought to validate the results from a machine learning based dysphagia risk prediction tool with clinical evaluation. METHODS: 149 inpatients in the ENT department were evaluated in real time by the risk prediction tool, as well as clinically over a 3-week period. Patients were classified by both as patients at risk/no risk. RESULTS: The AUROC, reflecting the discrimination capability of the algorithm, was 0.97. The accuracy achieved 92.6% given an excellent specificity as well as sensitivity of 98% and 82.4% resp. Higher age, as well as male sex and the diagnosis of oropharyngeal malignancies were found more often in patients at risk of dysphagia. CONCLUSION: The proposed dysphagia risk prediction tool proved to have an outstanding performance in discriminating risk from no risk patients in a prospective clinical setting. It is likely to be particularly useful in settings where there is a lower incidence of patients with dysphagia and less awareness among staff.
- Find related publications in this database (Keywords)
- Dysphagia screening
- Machine learning
- Real time evaluation