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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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