An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
| Autor: | Meng Cao, Yindalon Aphinyanaphongs, Jan Witowski, Carlos Fernandez-Granda, Yiqiu Shen, Siddhant Dogra, Duo Wang, Jungkyu Park, Narges Razavian, David Kudlowitz, Krzysztof J. Geras, Yvonne W. Lui, Farah E. Shamout, Nan Wu, Lea Azour, Aakash Kaku, Stanisław Jastrzębski, William Moore, Taro Makino, Ben Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Clinical variables Artificial Intelligence System Coronavirus disease 2019 (COVID-19) Computer science Computer Vision and Pattern Recognition (cs.CV) Computer applications to medicine. Medical informatics Computer Science - Computer Vision and Pattern Recognition R858-859.7 Medicine (miscellaneous) Health Informatics Article 030218 nuclear medicine & medical imaging Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine Health Information Management medicine FOS: Electrical engineering electronic engineering information engineering Receiver operating characteristic Artificial neural network Image and Video Processing (eess.IV) Computational science 030208 emergency & critical care medicine Emergency department Electrical Engineering and Systems Science - Image and Video Processing medicine.disease Triage 3. Good health Computer Science Applications Radiography Gradient boosting Medical emergency Biomedical engineering |
| Zdroj: | ArXiv npj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021) NPJ Digital Medicine |
| Popis: | 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-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 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) 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 New York University 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. |
| Databáze: | OpenAIRE |
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