Department of Computational Medicine & Bioinformatics
Prediction and Assessment of Acute Respiratory Distress Syndrome
Elyas Sabeti, Nick Reamaroon
Decision Support Systems
This project aims to create novel computational methodologies that synthesize and integrate longitudinal electronic health data streams for real-time and continuous health status monitoring and early detection of disease. We will utilize these technologies to address the problem of monitoring patients with lung disease to detect the presence of the acute respiratory distress syndrome (ARDS) at early stages. The project combines the capabilities of two emerging fields of machine learning, privileged learning and learning from uncertain data, both highly relevant to healthcare applications. While the current project focuses on improving diagnosis of ARDS, the proposed learning methods will generalize across healthcare settings, allowing for better characterization of patient health status in both in-hospital and in-home settings via portable electronic monitoring devices.