| Autor: |
LIENHART, Anna Maria, KRAMER, Diether, JAUK, Stefanie, GUGATSCHKA, Markus, LEODOLTER, Werner, SCHLEGL, Thomas |
| Zdroj: |
Studies in Health Technology & Informatics; 2020, Vol. 271, p31-38, 8p, 3 Charts, 1 Graph |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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