{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:25:39Z","timestamp":1761092739737,"version":"build-2065373602"},"reference-count":34,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1016\/j.acra.2021.05.002","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T14:31:07Z","timestamp":1621607467000},"page":"1151-1158","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning"],"prefix":"10.1016","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8108-4706","authenticated-orcid":false,"given":"Ayis","family":"Pyrros","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam Eugene","family":"Flanders","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge Mario","family":"Rodr\u00edguez-Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Cole","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Wenzke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Hart","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Harford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeanne","family":"Horowitz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Nikolaidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadir","family":"Muzaffar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viveka","family":"Boddipalli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jai","family":"Nebhrajani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nasir","family":"Siddiqui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melinda","family":"Willis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houshang","family":"Darabi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oluwasanmi","family":"Koyejo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Galanter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2021.05.002_bib0001","doi-asserted-by":"crossref","unstructured":"Huang C, Wang Y, Li X, et\u00a0al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 2020;395(10223):497\u2013506. doi: 10.1016\/S0140-6736(20)30183-5","DOI":"10.1016\/S0140-6736(20)30183-5"},{"issue":"20","key":"10.1016\/j.acra.2021.05.002_bib0002","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1001\/jama.2020.6775","article-title":"Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area","volume":"323","author":"Richardson","year":"2020","journal-title":"JAMA"},{"key":"10.1016\/j.acra.2021.05.002_bib0003","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ijid.2020.03.017","article-title":"Prevalence of comorbidities and its effects in patients infected with SARS-Cov-2: a systematic review and meta-analysis","volume":"94","author":"Yang","year":"2020","journal-title":"Int J Infect Dis"},{"issue":"2","key":"10.1016\/j.acra.2021.05.002_bib0004","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1097\/QMH.0000000000000052","article-title":"Pocket change: a simple educational intervention increases hospitalist documentation of comorbidities and improves hospital quality performance measures","volume":"24","author":"Sparks","year":"2015","journal-title":"Qual Manag Health Care."},{"key":"10.1016\/j.acra.2021.05.002_bib0005","first-page":"1","article-title":"The role of initial chest X-ray in triaging patients with suspected COVID-19 during the pandemic","author":"Kim","year":"2020","journal-title":"Emerg Radiol"},{"issue":"4","key":"10.1016\/j.acra.2021.05.002_bib0006","doi-asserted-by":"crossref","DOI":"10.5334\/ijic.2500","article-title":"A review on methods of risk adjustment and their use in integrated healthcare systems","volume":"16","author":"Juhnke","year":"2020","journal-title":"Int J Integr Care"},{"issue":"1","key":"10.1016\/j.acra.2021.05.002_bib0007","doi-asserted-by":"crossref","first-page":"E197","DOI":"10.1148\/radiol.2020201754","article-title":"Clinical and chest radiography features determine patient outcomes in young and middle-aged adults with COVID-19","volume":"297","author":"Toussie","year":"2020","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.acra.2021.05.002_bib0008","doi-asserted-by":"crossref","first-page":"E46","DOI":"10.1148\/radiol.2020202723","article-title":"Model-based prediction of critical illness in hospitalized patients with COVID-19","volume":"298","author":"Schalekamp","year":"2021","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2021.05.002_bib0009","unstructured":"Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv:14126980 [cs] [Internet]. 2017 [cited 2020 Nov 17]; Available at: http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"10.1016\/j.acra.2021.05.002_bib0010","unstructured":"Liu R, Lehman J, Molino P, et\u00a0al. An intriguing failing of convolutional neural networks and the CoordConv solution. arXiv:180703247 [cs, stat] [Internet]. 2018 [cited 2020 Nov 17]; Available at: http:\/\/arxiv.org\/abs\/1807.03247"},{"key":"10.1016\/j.acra.2021.05.002_bib0011","unstructured":"Irvin J, Rajpurkar P, Ko M, et\u00a0al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. arXiv:190107031 [cs, eess] [Internet]. 2019 [cited 2020 Nov 17]; Available at: http:\/\/arxiv.org\/abs\/1901.07031"},{"key":"10.1016\/j.acra.2021.05.002_bib0012","series-title":"Proceedings of the Tenth International Conference on International Conference on Machine Learning (ICML\u201993","first-page":"41","article-title":"Multitask learning: a knowledge-based source of inductive bias","author":"Caruana","year":"1993"},{"issue":"3","key":"10.1016\/j.acra.2021.05.002_bib0013","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s10994-007-5040-8","article-title":"Convex multi-task feature learning","volume":"73","author":"Argyriou","year":"2008","journal-title":"Mach Learn"},{"issue":"1","key":"10.1016\/j.acra.2021.05.002_bib0014","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41597-019-0103-9","article-title":"Multitask learning and benchmarking with clinical time series data","volume":"6","author":"Harutyunyan","year":"2019","journal-title":"Scientific Data"},{"key":"10.1016\/j.acra.2021.05.002_bib0015","series-title":"Proceedings of the 25th International Conference on Machine Learning - ICML \u201908","first-page":"160","article-title":"A unified architecture for natural language processing: deep neural networks with multitask learning","author":"Collobert","year":"2008"},{"key":"10.1016\/j.acra.2021.05.002_bib0016","doi-asserted-by":"crossref","unstructured":"Wang L, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. arXiv:200309871 [cs, eess] [Internet]. 2020 [cited 2020 Nov 17]; Available at: http:\/\/arxiv.org\/abs\/2003.09871","DOI":"10.1038\/s41598-020-76550-z"},{"key":"10.1016\/j.acra.2021.05.002_bib0017","series-title":"Applied Logistic Regression","first-page":"528","author":"Hosmer","year":"2013"},{"issue":"3","key":"10.1016\/j.acra.2021.05.002_bib0018","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier methodology","volume":"21","author":"Safavian","year":"1991","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"issue":"3","key":"10.1016\/j.acra.2021.05.002_bib0019","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"10.1016\/j.acra.2021.05.002_bib0020","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916)","first-page":"785","article-title":"XGBoost: a scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.acra.2021.05.002_bib0021","series-title":"NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"3146","article-title":"LightGBM: a highly efficient gradient boosting decision tree","author":"Ke","year":"2017"},{"key":"10.1016\/j.acra.2021.05.002_bib0022","series-title":"NIPS\u201918: Proceedings of the 32nd International Conference on Neural Information Processing Systems","first-page":"6639","article-title":"CatBoost: unbiased boosting with categorical features","author":"Prokhorenkova","year":"2018"},{"key":"10.1016\/j.acra.2021.05.002_bib0023","unstructured":"Sundararajan M, Taly, A, Ya Q. Axiomatic attribution for deep networks. arXiv: 1703.01365 [CoRR][Internet]. 2018 [cited 2021 Jan 5]; Available at: http:\/\/arxiv.org\/abs\/1703.01365"},{"key":"10.1016\/j.acra.2021.05.002_bib0024","doi-asserted-by":"crossref","first-page":"m1328","DOI":"10.1136\/bmj.m1328","article-title":"Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal","volume":"369","author":"Wynants","year":"2020","journal-title":"BMJ"},{"issue":"11","key":"10.1016\/j.acra.2021.05.002_bib0025","article-title":"Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: the veterans health administration COVID-19 (VACO) index","volume":"15","author":"King Jr","year":"2020","journal-title":"PLoS One."},{"issue":"3","key":"10.1016\/j.acra.2021.05.002_bib0026","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1148\/radiol.2020200370","article-title":"Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19)","volume":"295","author":"Pan","year":"2020","journal-title":"Radiology"},{"issue":"2","key":"10.1016\/j.acra.2021.05.002_bib0027","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1159\/000495786","article-title":"Relationship between aortic arch calcification, detected by chest x-ray, and renal resistive index in patients with hypertension","volume":"28","author":"Adar","year":"2019","journal-title":"Med Princ Pract"},{"issue":"2","key":"10.1016\/j.acra.2021.05.002_bib0028","doi-asserted-by":"crossref","first-page":"122","DOI":"10.5853\/jos.2013.15.2.122","article-title":"Association of aortic knob calcification with intracranial stenosis in ischemic stroke patients","volume":"15","author":"Kim","year":"2013","journal-title":"J Stroke"},{"issue":"7","key":"10.1016\/j.acra.2021.05.002_bib0029","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2019.7416","article-title":"Deep learning to assess long-term mortality from chest radiographs","volume":"2","author":"Lu","year":"2019","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.acra.2021.05.002_bib0030","doi-asserted-by":"crossref","unstructured":"Karargyris A, Kashyap S, Wu JT, Sharma A, Moradi M, Syeda-Mahmood T. Age prediction using a large chest x-ray dataset. arXiv:190306542 [cs] [Internet]. 2019 [cited 2020 Nov 17]; Available at: http:\/\/arxiv.org\/abs\/1903.06542","DOI":"10.1117\/12.2512922"},{"issue":"3","key":"10.1016\/j.acra.2021.05.002_bib0031","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1093\/ije\/dyr041","article-title":"The Simpson's paradox unraveled","volume":"40","author":"Hern\u00e1n","year":"2011","journal-title":"Int J Epidemiol"},{"key":"10.1016\/j.acra.2021.05.002_bib0032","doi-asserted-by":"crossref","first-page":"e168","DOI":"10.1017\/S0950268820001727","article-title":"Development and validation of prognosis model of mortality risk in patients with COVID-19","volume":"148","author":"Ma","year":"2020","journal-title":"Epidemiol Infect"},{"issue":"2","key":"10.1016\/j.acra.2021.05.002_bib0033","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.amepre.2020.05.002","article-title":"Clinical characteristics, associated factors, and predicting COVID-19 mortality risk: A retrospective study in Wuhan, China","volume":"59","author":"Yu","year":"2020","journal-title":"Am J Prev Med"},{"key":"10.1016\/j.acra.2021.05.002_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2019.103255","article-title":"The value of missing information in severity of illness score development","volume":"97","author":"Agor","year":"2019","journal-title":"J Biomed Inform"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633221002178?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633221002178?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T08:50:40Z","timestamp":1761036640000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633221002178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":34,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["S1076633221002178"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2021.05.002","relation":{},"ISSN":["1076-6332"],"issn-type":[{"type":"print","value":"1076-6332"}],"subject":[],"published":{"date-parts":[[2021,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2021.05.002","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}