Data fusion for predicting ARDS using the MIMIC II physiological database

Abstract

This study aims to predict Acute Respiratory Distress Syndrome (ARDS) in hospitalized patients using their physiological signals such as heart rate, breathing rate, peripheral arterial oxygen saturation and mean airway blood pressure. A data fusion approach based on hypothesis testing was developed, and applied to mechanically ventilated subjects in the MIMIC II database. By combining the information extracted from the signals using an aggregation rule, we are able to enhance the sensitivity of the ARDS prediction process. As a result, we obtained a sensitivity of up to 85% for individual signals, reaching approximately 92% using the data fusion rule.