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- Title
Clinical evaluation of a machine learning-based dysphagia risk prediction tool.
- Authors
Gugatschka, Markus; Egger, Nina Maria; Haspl, K.; Hortobagyi, David; Jauk, Stefanie; Feiner, Marlies; Kramer, Diether
- 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.
- Subjects
MACHINE learning; FORECASTING; DEGLUTITION disorders
- Publication
European Archives of Oto-Rhino-Laryngology, 2024, Vol 281, Issue 8, p4379
- ISSN
0937-4477
- Publication type
Article
- DOI
10.1007/s00405-024-08678-x