AI Article Synopsis
- A study was conducted to validate a machine learning tool designed to predict the risk of dysphagia (difficulty swallowing) in hospitalized patients, comparing its results with clinical evaluations.
- A total of 149 inpatients in the ENT department were assessed over three weeks, showing the algorithm's performance with an AUROC score of 0.97 and an accuracy of 92.6%.
- Findings indicated that older age, male sex, and oropharyngeal malignancies increased the likelihood of being at risk for dysphagia, suggesting the tool could greatly aid in identifying at-risk patients in clinical settings with low dysphagia awareness.
Article 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266195 | PMC |
| http://dx.doi.org/10.1007/s00405-024-08678-x | DOI Listing |
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