AI Article Synopsis
- * The study focused on creating a predictive model for dysphagia risk in hospitalized patients using machine learning techniques applied to data from over 33,000 electronic health records.
- * The top-performing models, Random Forest and Adaboost classifiers, demonstrated high accuracy with an area under the curve of 0.94, surpassing existing dysphagia prediction models, and future integration into clinical practice is suggested for evaluating benefits.
Article 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.
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| http://dx.doi.org/10.3233/SHTI200071 | DOI Listing |
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