{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:16:23Z","timestamp":1740143783204,"version":"3.37.3"},"reference-count":12,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11517-022-02549-5","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:02:25Z","timestamp":1652227345000},"page":"2039-2049","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Who was at risk for COVID-19 late in the US pandemic? Insights from a population health machine learning model"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4203-8499","authenticated-orcid":false,"given":"Elijah A.","family":"Adeoye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yelena","family":"Rozenfeld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer","family":"Beam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen","family":"Boudreau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily J.","family":"Cox","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James M.","family":"Scanlan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"2549_CR1","unstructured":"Eisenstein M (2020) What\u2019s your risk of catching COVID? These tools help you to find out. June 15, 2021]; Available from: https:\/\/www.nature.com\/articles\/d41586-020-03637-y"},{"issue":"1","key":"2549_CR2","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1186\/s12939-020-01242-z","volume":"19","author":"Y Rozenfeld","year":"2020","unstructured":"Rozenfeld Y et al (2020) A model of disparities: risk factors associated with COVID-19 infection. Int J Equity Health 19(1):126","journal-title":"Int J Equity Health"},{"issue":"2","key":"2549_CR3","doi-asserted-by":"publisher","first-page":"e16","DOI":"10.1016\/j.jinf.2020.04.021","volume":"81","author":"Z Zheng","year":"2020","unstructured":"Zheng Z et al (2020) Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis. J Infect 81(2):e16\u2013e25","journal-title":"J Infect"},{"issue":"1","key":"2549_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s15010-020-01509-1","volume":"49","author":"D Wolff","year":"2021","unstructured":"Wolff D et al (2021) Risk factors for Covid-19 severity and fatality: a structured literature review. Infection 49(1):15\u201328","journal-title":"Infection"},{"issue":"1","key":"2549_CR5","doi-asserted-by":"publisher","first-page":"e23811","DOI":"10.2196\/23811","volume":"9","author":"HB Syeda","year":"2021","unstructured":"Syeda HB et al (2021) Role of machine learning techniques to tackle the COVID-19 crisis: systematic review. JMIR Med Inform 9(1):e23811","journal-title":"JMIR Med Inform"},{"issue":"5","key":"2549_CR6","doi-asserted-by":"publisher","first-page":"2655","DOI":"10.1007\/s40747-021-00424-8","volume":"7","author":"O Dogan","year":"2021","unstructured":"Dogan O, Tiwari S, Jabbar MA, Guggari S (2021) A systematic review on AI\/ML approaches against COVID-19 outbreak. Complex Intell Systems 7(5):2655\u20132678. https:\/\/doi.org\/10.1007\/s40747-021-00424-8","journal-title":"Complex Intell Systems"},{"key":"2549_CR7","doi-asserted-by":"publisher","first-page":"100495","DOI":"10.1016\/j.eclinm.2020.100495","volume":"26","author":"AA Malik","year":"2020","unstructured":"Malik AA et al (2020) Determinants of COVID-19 vaccine acceptance in the US. EClinicalMedicine 26:100495","journal-title":"EClinicalMedicine"},{"issue":"2","key":"2549_CR8","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1007\/s10900-020-00958-x","volume":"46","author":"J Khubchandani","year":"2021","unstructured":"Khubchandani J et al (2021) COVID-19 vaccination hesitancy in the United States: a rapid national assessment. J Community Health 46(2):270\u2013277","journal-title":"J Community Health"},{"key":"2549_CR9","unstructured":"Brownlee J (2020) SMOTE for imbalanced classification with Python. machine learning mastery. Available from: https:\/\/machinelearningmastery.com\/smote-oversampling-for-imbalanced-classification"},{"key":"2549_CR10","unstructured":"Lundberg S (2018) Welcome to the SHAP documentation \u2014 SHAP latest documentation. SHAP. Available from: https:\/\/shap.readthedocs.io\/en\/latest\/"},{"key":"2549_CR11","unstructured":"Molnar C (2020) 5.10 SHAP (SHapley Additive exPlanations) | Interpretable machine learning. SHAP (SHapley Additive exPlanations). [cited 2021; Available from: https:\/\/christophm.github.io\/interpretable-ml-book\/shap.html"},{"key":"2549_CR12","doi-asserted-by":"publisher","unstructured":"Mickael T, Ahmed M, Rahul MD, Ines S, Guillaume C, Enora G, Robert B, Deborah E, Sebastien B, Guillaume M, Simon B, Antoine F, Fadila M, Benoit D, Robert-Yves C, Luc DJ, Marie-Pierre R (2020) Pre-test probability for SARS-Cov-2-related infection score: the PARIS score.\u00a0medRxiv 2020.04.28.20081687.\u00a0https:\/\/doi.org\/10.1101\/2020.04.28.20081687","DOI":"10.1101\/2020.04.28.20081687"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02549-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02549-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02549-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T03:38:58Z","timestamp":1656041938000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02549-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":12,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["2549"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02549-5","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2022,5,11]]},"assertion":[{"value":"14 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}