Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning.
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 reduc...
| Publicado en: | Studies in Health Technology & Informatics Vol. 271; pp. 31 - 39 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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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