Medizinische Universität Graz - Research portal

Go directly to the nagivation.
Go directly to the content.

Navigation/Search

Selected Publication:

SHR Neuro Cancer Cardio Lipid Metab Microb

Lienhart, AM; Kramer, D; Jauk, S; Gugatschka, M; Leodolter, W; Schlegl, T.
Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning.
Stud Health Technol Inform. 2020; 271: 31-38. Doi: 10.3233/SHTI200071
PubMed FullText FullText_MUG

Co-authors Med Uni Graz: Gugatschka Markus
Altmetrics:
Dimensions Citations:
Plum Analytics:
Scite (citation analytics):
Abstract:: 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. The aim of the study was to develop a multivariable model predicting the individual risk of dysphagia in hospitalized patients. We trained different machine learning algorithms on the electronic health records of over 33,000 patients. The tree-based Random Forest Classifier and Adaboost Classifier algorithms achieved an area under the receiver operating characteristic curve of 0.94. The developed models outperformed previously published models predicting dysphagia. In future, an implementation in the clinical workflow is needed to determine the clinical benefit.