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Updated: Dec 17, 2025
Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
Published on: March 1, 2015
Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning
Anna Maria Lienhart1, Diether Kramer1, Stefanie Jauk1
1Styrian Hospitals Limited Liability Company (KAGes), Graz, Austria.
Background:
Dysphagia is a dysfunction of the swallowing act and is highly prevalent in acute post-stroke patients and patients with chronic neurological diseases. Dysphagia is associated with several potentially life threatening complications. Thus, an early identification and treatment could reduce morbidity and mortality rates.
Objectives:
The aim of the study was to develop a multivariable model predicting the individual risk of dysphagia in hospitalized patients.
Methods:
We trained different machine learning algorithms on the electronic health records of over 33,000 patients.
Results:
The tree-based Random Forest Classifier and Adaboost Classifier algorithms achieved an area under the receiver operating characteristic curve of 0.94.
Conclusion:
The developed models outperformed previously published models predicting dysphagia. In future, an implementation in the clinical workflow is needed to determine the clinical benefit.
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