An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency departmentDownload PDF

Apr 06, 2021 (edited Apr 20, 2021)MIDL 2021 Conference Short SubmissionReaders: Everyone
  • 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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  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
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