About: An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

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About: An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Creator	Moore, William Wu, Nan Azour, Lea Dogra, Siddhant Zhang, Ben Cao, Meng Aphinyanaphongs, Yindalon Fernandez-Granda, Carlos Geras, Krzysztof Jastrzębski, Stanisław Kaku, Aakash Kudlowitz, David Lui, Yvonne Makino, Taro Park, Jungkyu Razavian, Narges Shamout, Farah Shen, Yiqiu Wang, Duo
Source	ArXiv
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 automatic 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 an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
has issue date	2020-08-04(xsd:dateTime)
has license	arxiv
sha1sum (hex)	364890eae890b77252db53e68955b4ab034ae5eb
resource representing a document's title	An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
resource representing a document's body	covid:364890eae890b77252db53e68955b4ab034ae5eb#body_text
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