Detection of Acute Respiratory Distress Syndrome by Incorporation of Label Uncertainty and Partially Available Privileged Information

Acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that develops in patients with critical illnesses including sepsis, pneumonia, and trauma. However, many patients with ARDS are not recognized when they develop this syndrome nor given outcome-improving treatments. Because ARDS is a clinical syndrome, physicians may not be certain about a patient’s diagnosis (label uncertainty). In addition, the diagnosis requires a chest x-ray, which may not be always be available in a clinical setting (privileged information). For this paper, we implemented the Learning Using Label Uncertainty and Partially Available Privileged Information (LULUPAPI) paradigm, built on classical SVM, to detect ARDS using Electronic Health Record (EHR) data and chest radiography. In comparison to SVM, this resulted in a 3.55 percent improvement of test AUC.