Abstract: During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves the AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. Our deep neural network indicates informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two experienced chest radiologists in a reader study. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
Paper Type: validation/application paper
Primary Subject Area: Application: Radiology
Secondary Subject Area: Integration of Imaging and Clinical Data
Paper Status: based on accepted/submitted journal paper
Source Code Url: https://github.com/nyukat/COVID-19_prognosis
Data Set Url: The COVID-19 X-ray images and associated clinical variables from NYU Langone Health are not publicly available, but we provide sample patients in our source code repository.
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Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2008.01774/code)