Clinical evaluation of a machine learning-based dysphagia risk prediction tool.
Dysphagia screening
Machine learning
Real time evaluation
Journal
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
ISSN: 1434-4726
Titre abrégé: Eur Arch Otorhinolaryngol
Pays: Germany
ID NLM: 9002937
Informations de publication
Date de publication:
14 May 2024
14 May 2024
Historique:
received:
22
01
2024
accepted:
12
04
2024
medline:
15
5
2024
pubmed:
15
5
2024
entrez:
14
5
2024
Statut:
aheadofprint
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 38743079
doi: 10.1007/s00405-024-08678-x
pii: 10.1007/s00405-024-08678-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
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