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

Abstract

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% had OD. The psychometrics of the ES built with a non-linear model were: Area under the ROC Curve of 0.840; sensitivity 0.940; specificity, 0.416; Positive Predictive Value 0.834; Negative Predictive Value 0.690; positive likelihood ratio (LH), 1.61 and negative LH, 0.146. The ES screens in 6 s all patients admitted to a 419-bed hospital, identifies patients at greater risk of OD, and shows the risk for OD in the clinician's workstation. It is currently in use at our institution. Our ES provides accurate, systematic and universal screening for OD in real time during hospital admission of older patients, allowing the most appropriate diagnostic and therapeutic strategies to be selected for each patient.

Keywords: Aging; Diagnostics; Dysphagia; Machine learning; Screening methods; Swallowing disorders.