Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning

Lienhart, Anna Maria; Kramer, Diether; Jauk, Stefanie; Gugatschka, Markus; Leodolter, Werner; Schlegl, Thomas

doi:10.3233/SHTI200071

Abstract

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.