33 citations · 42 across the 4 of their papers we have counts for
5 papers
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in deci…
Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
Nasir Hayat, Hazem Lashen, Farah E. Shamout
Despite the success of deep neural networks in chest X-ray (CXR) diagnosis, supervised learning only allows the prediction of disease classes that were seen during training. At inf…
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction
Anuroop Sriram, Matthew Muckley, Koustuv Sinha +7
The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. M…
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study
Ghadeer O. Ghosheh, Bana Alamad, Kai-Wen Yang +10
Existing prognostic tools mainly focus on predicting the risk of mortality among patients with coronavirus disease 2019. However, clinical evidence suggests that COVID-19 can resul…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…