A Systematic and Universal Artificial Intelligence Screening Method for Oropharyngeal Dysphagia: Improving Diagnosis Through Risk Management

Oropharyngeal dysphagia (OD) is underdiagnosed and current screening is costly. We aimed: (a) to develop an expert system (ES) based on machine learning that calculates the risk of OD from the electronic health records (EHR) of all hospitalized older patients during admission, and (b) to implement the ES in a general hospital. In an observational, retrospective study, EHR and swallowing assessment using the volume-viscosity swallow test for OD were captured over 24 months in patients > 70 yr admitted to Mataró Hospital. We studied the predictive power for OD of 25,000 variables. ES was obtained using feature selection, the final prediction model was built with non-linear methods (Random Forest). The database included 2809 older patients (mean age 82.47 ± 9.33 yr), severely dependent (Barthel Index 47.68 ± 31.90), with multiple readmissions (4.06 ± 7.52); 75.76