A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters.

ABSTRACT: Early diagnosis and prevention play a crucial role in the treatment of patients with ARDS. The definition of ARDS requires an arterial blood gas to define the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2 ratio). However, many patients with ARDS do not have a blood gas measured, which may result in under-diagnosis of the condition. Using data from MIMIC-III Database, we propose an algorithm based on patient non-invasive physiological parameters to estimate P/F levels to aid in the diagnosis of ARDS disease. The machine learning algorithm was combined with the filter feature selection method to study the correlation of various noninvasive parameters from patients to identify the ARDS disease. Cross-validation techniques are used to verify the perform

SUBMITTER: Yang P 

PROVIDER: S-EPMC7001976 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature