Abstract: 

There is a pressing need to identify deterioration amongst patients with COVID-19 in order to avoid life-threatening adverse events. Chest radiographs are frequently collected from patients presenting with COVID-19 upon arrival to the emergency department since it is considered as a first-line triage tool and the disease primarily manifests as a respiratory illness. In this talk, I will discuss the AI prognosis system we developed using data collected at NYU Langone Health to predict in-hospital deterioration, defined as the occurrence of intubation, mortality, or ICU admission. In particular, our system consists of an ensemble of an interpretable deep learning model to learn from chest X-ray images and a gradient boosting model to learn from routinely collected clinical variables, e.g. vital signs and laboratory tests. The system also computes deterioration risk curves to summarise how the risk is expected to evolve over time. The results of retrospective validation on the held-out test set, the reader study, and silent deployment in the hospital infrastructure highlight the promise of our AI system in assisting front-line workers through real-time assessment of prognosis.

Bio: 

Farah Shamout is an Assistant Professor Emerging Scholar in Computer Engineering at NYU Abu Dhabi. Her research expertise is in machine learning for healthcare, data analytics for large-scale multi-modal data, and model interpretability. Her projects focus on real-world clinical problems to inform decision-making, including diagnosis and prognosis using electronic health records and medical imaging. Previously, Farah completed her doctoral studies in Engineering Science at the University of Oxford as a Rhodes Scholar.