An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Abstract 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 che...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/39490bddaa344a02b06a3d77ab46dd52 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:39490bddaa344a02b06a3d77ab46dd52 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:39490bddaa344a02b06a3d77ab46dd522021-12-02T15:55:22ZAn artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department10.1038/s41746-021-00453-02398-6352https://doaj.org/article/39490bddaa344a02b06a3d77ab46dd522021-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00453-0https://doaj.org/toc/2398-6352Abstract 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.Farah E. ShamoutYiqiu ShenNan WuAakash KakuJungkyu ParkTaro MakinoStanisław JastrzębskiJan WitowskiDuo WangBen ZhangSiddhant DograMeng CaoNarges RazavianDavid KudlowitzLea AzourWilliam MooreYvonne W. LuiYindalon AphinyanaphongsCarlos Fernandez-GrandaKrzysztof J. GerasNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Computer applications to medicine. Medical informatics R858-859.7 |
| spellingShingle |
Computer applications to medicine. Medical informatics R858-859.7 Farah E. Shamout Yiqiu Shen Nan Wu Aakash Kaku Jungkyu Park Taro Makino Stanisław Jastrzębski Jan Witowski Duo Wang Ben Zhang Siddhant Dogra Meng Cao Narges Razavian David Kudlowitz Lea Azour William Moore Yvonne W. Lui Yindalon Aphinyanaphongs Carlos Fernandez-Granda Krzysztof J. Geras An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department |
| description |
Abstract 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. |
| format |
article |
| author |
Farah E. Shamout Yiqiu Shen Nan Wu Aakash Kaku Jungkyu Park Taro Makino Stanisław Jastrzębski Jan Witowski Duo Wang Ben Zhang Siddhant Dogra Meng Cao Narges Razavian David Kudlowitz Lea Azour William Moore Yvonne W. Lui Yindalon Aphinyanaphongs Carlos Fernandez-Granda Krzysztof J. Geras |
| author_facet |
Farah E. Shamout Yiqiu Shen Nan Wu Aakash Kaku Jungkyu Park Taro Makino Stanisław Jastrzębski Jan Witowski Duo Wang Ben Zhang Siddhant Dogra Meng Cao Narges Razavian David Kudlowitz Lea Azour William Moore Yvonne W. Lui Yindalon Aphinyanaphongs Carlos Fernandez-Granda Krzysztof J. Geras |
| author_sort |
Farah E. Shamout |
| title |
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department |
| title_short |
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department |
| title_full |
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department |
| title_fullStr |
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department |
| title_full_unstemmed |
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department |
| title_sort |
artificial intelligence system for predicting the deterioration of covid-19 patients in the emergency department |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/39490bddaa344a02b06a3d77ab46dd52 |
| work_keys_str_mv |
AT faraheshamout anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT yiqiushen anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT nanwu anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT aakashkaku anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT jungkyupark anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT taromakino anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT stanisławjastrzebski anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT janwitowski anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT duowang anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT benzhang anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT siddhantdogra anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT mengcao anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT nargesrazavian anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT davidkudlowitz anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT leaazour anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT williammoore anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT yvonnewlui anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT yindalonaphinyanaphongs anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT carlosfernandezgranda anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT krzysztofjgeras anartificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT faraheshamout artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT yiqiushen artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT nanwu artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT aakashkaku artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT jungkyupark artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT taromakino artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT stanisławjastrzebski artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT janwitowski artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT duowang artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT benzhang artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT siddhantdogra artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT mengcao artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT nargesrazavian artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT davidkudlowitz artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT leaazour artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT williammoore artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT yvonnewlui artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT yindalonaphinyanaphongs artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT carlosfernandezgranda artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment AT krzysztofjgeras artificialintelligencesystemforpredictingthedeteriorationofcovid19patientsintheemergencydepartment |
| _version_ |
1718385385047851008 |