{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T18:45:07Z","timestamp":1757616307813,"version":"3.44.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61872225"],"award-info":[{"award-number":["No. 61872225"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["NO.ZR2020KF013","No.ZR2020ZD44","No.ZR2019ZD04","No.ZR2020QF043","No.ZR2022QG051"],"award-info":[{"award-number":["NO.ZR2020KF013","No.ZR2020ZD44","No.ZR2019ZD04","No.ZR2020QF043","No.ZR2022QG051"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Introduction and Cultivation Program for Young CreativeTalents in Colleges and Universities of Shandong Province","award":["No.2019-173"],"award-info":[{"award-number":["No.2019-173"]}]},{"name":"the Special fund of Qilu Health and Health Leading Talents Training Project","award":["2021"],"award-info":[{"award-number":["2021"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s11517-024-03029-8","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:02:12Z","timestamp":1708041732000},"page":"2485-2495","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A conformal regressor for predicting negative conversion time of Omicron patients"],"prefix":"10.1007","volume":"63","author":[{"given":"Pingping","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenjing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunmeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyu","family":"Cong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9640-4947","authenticated-orcid":false,"given":"Benzheng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"issue":"7888","key":"3029_CR1","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1126\/science.acx9782","volume":"600","author":"E Callaway","year":"2021","unstructured":"Callaway E, Ledford H et al (2021) How bad is Omicron? What Sci Know Nat 600(7888):197\u2013199. https:\/\/doi.org\/10.1126\/science.acx9782","journal-title":"What Sci Know Nat"},{"issue":"5","key":"3029_CR2","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1002\/jmv.27541","volume":"94","author":"V Thakur","year":"2022","unstructured":"Thakur V, Ratho RK (2022) OMICRON (B. 1.1. 529): a new SARS-CoV-2 variant of concern mounting worldwide fear. J Med Virol 94(5):1821\u20131824. https:\/\/doi.org\/10.1002\/jmv.27541","journal-title":"J Med Virol"},{"issue":"9","key":"3029_CR3","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.3390\/vaccines10091409","volume":"10","author":"J Shao","year":"2022","unstructured":"Shao J, Fan R, Hu J, Zhang T, Lee C, Huang X, Wang F, Liang H, Jin Y, Jiang Y et al (2022) Clinical progression and outcome of hospitalized patients infected with SARS-CoV-2 Omicron variant in shanghai, china. Vaccines 10(9):1409. https:\/\/doi.org\/10.3390\/vaccines10091409","journal-title":"Vaccines"},{"issue":"1","key":"3029_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41392-022-00997-x","volume":"7","author":"Y Fan","year":"2022","unstructured":"Fan Y, Li X, Zhang L, Wan S, Zhang L, Zhou F (2022) SARS-CoV-2 Omicron variant: recent progress and future perspectives. Signal Transduct Target Ther 7(1):1\u201311. https:\/\/doi.org\/10.1038\/s41392-022-00997-x","journal-title":"Signal Transduct Target Ther"},{"issue":"6","key":"3029_CR5","doi-asserted-by":"publisher","first-page":"2376","DOI":"10.1002\/jmv.27643","volume":"94","author":"D Tian","year":"2022","unstructured":"Tian D, Sun Y, Xu H, Ye Q (2022) The emergence and epidemic characteristics of the highly mutated SARS-CoV-2 Omicron variant. J Med Virol 94(6):2376\u20132383. https:\/\/doi.org\/10.1002\/jmv.27643","journal-title":"J Med Virol"},{"key":"3029_CR6","doi-asserted-by":"publisher","unstructured":"Martin-Blondel G, Marcelin AG, Souli\u00b4e C, Kaisaridi S, Lusivika Nzinga C, Zafilaza K, Dorival C, Nailler L, Boston A, Ronchetti AM, et al. (2022) Time to negative PCR conversion amongst high-risk patients with mild-to-moderate Omicron BA. 1 and BA. 2 COVID-19 treated with sotrovimab or nirmatrelvir. Clinical Microbiology and Infection. https:\/\/doi.org\/10.1016\/j.cmi.2022.12.016","DOI":"10.1016\/j.cmi.2022.12.016"},{"issue":"4","key":"3029_CR7","doi-asserted-by":"publisher","first-page":"e28747","DOI":"10.1002\/jmv.28747","volume":"95","author":"J Ye","year":"2023","unstructured":"Ye J, Shao X, Yang Y, Zhu F (2023) Predicting the negative conversion time of nonsevere COVID-19 patients using machine learning methods. J Med Virol 95(4):e28747","journal-title":"J Med Virol"},{"issue":"8","key":"3029_CR8","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1109\/TMI.2020.2996256","volume":"39","author":"Z Han","year":"2020","unstructured":"Han Z, Wei B, Hong Y, Li T, Cong J, Zhu X, Wei H, Zhang W (2020) Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Trans Med Imaging 39(8):2584\u20132594. https:\/\/doi.org\/10.1109\/TMI.2020.2996256","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"3029_CR9","doi-asserted-by":"publisher","first-page":"3786","DOI":"10.1109\/TNNLS.2021.3099165","volume":"32","author":"G Liu","year":"2021","unstructured":"Liu G, Liao Y, Wang F, Zhang B, Zhang L, Liang X, Wan X, Li S, Li Z, Zhang S et al (2021) Medical-vlbert: medical visual language bert for COVID -19 CT report generation with alternate learning. IEEE Trans Neural Net Learn Syst 32(9):3786\u20133797. https:\/\/doi.org\/10.1109\/TNNLS.2021.3099165","journal-title":"IEEE Trans Neural Net Learn Syst"},{"key":"3029_CR10","doi-asserted-by":"publisher","first-page":"107323","DOI":"10.1016\/j.asoc.2021.107323","volume":"105","author":"A Sayg\u0131l\u0131","year":"2021","unstructured":"Sayg\u0131l\u0131 A (2021) A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods. Appl Soft Comput 105:107323. https:\/\/doi.org\/10.1016\/j.asoc.2021.107323","journal-title":"Appl Soft Comput"},{"key":"3029_CR11","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.neucom.2022.12.017","volume":"523","author":"Y Sun","year":"2023","unstructured":"Sun Y, Cong J, Zhang K, Jian M, Wei B (2023) Unsupervised medical image feature learning by using de-melting reduction auto-encoder. Neurocomputing 523:145\u2013156. https:\/\/doi.org\/10.1016\/j.neucom.2022.12.017","journal-title":"Neurocomputing"},{"issue":"2","key":"3029_CR12","first-page":"199","volume":"9","author":"F Nurkholis","year":"2022","unstructured":"Nurkholis F, Wibisono BH, Suryanto A, Handoyo T, Farida F, Tanamas J (2022) Factors influencing the delay in negative conversion of pcr swab test results in patients with COVID-19. Medica Hospitalia. J Clin Med 9(2):199\u2013206","journal-title":"J Clin Med"},{"key":"3029_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2022.3226575","author":"Y Liu","year":"2022","unstructured":"Liu Y, Li H, Luo T, Zhang C, Xiao Z, Wei Y, Gao Y, Shi F, Shan F, Shen D (2022) Structural attention graph neural network for diagnosis and prediction of COVID-19 severity. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1109\/tmi.2022.3226575","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"3029_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-00372-6","volume":"4","author":"Y Zoabi","year":"2021","unstructured":"Zoabi Y, Deri-Rozov S, Shomron N (2021) Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj digital medicine 4(1):1\u20135. https:\/\/doi.org\/10.1038\/s41746-020-00372-6","journal-title":"npj digital medicine"},{"key":"3029_CR15","unstructured":"Vovk V, Gammerman A, Shafer G (2005) Algorithmic learning in a random world. Springer, New York"},{"key":"3029_CR16","volume-title":"Conformal prediction for reliable machine learning: theory, adaptations and applications","author":"V Balasubramanian","year":"2014","unstructured":"Balasubramanian V, Ho SS, Vovk V (2014) Conformal prediction for reliable machine learning: theory, adaptations and applications. Newnes"},{"key":"3029_CR17","first-page":"371","volume":"9","author":"G Shafer","year":"2008","unstructured":"Shafer G, Vovk V (2008) A tutorial on conformal prediction. J Mach Learn Res 9:371\u2013421","journal-title":"J Mach Learn Res"},{"key":"3029_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2018.04.012","volume":"304","author":"D Wang","year":"2018","unstructured":"Wang D, Wang P, Shi J (2018) A fast and efficient conformal regressor with regularized extreme learning machine. Neurocomputing 304:1\u201311. https:\/\/doi.org\/10.1016\/j.neucom.2018.04.012","journal-title":"Neurocomputing"},{"key":"3029_CR19","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.neunet.2020.03.022","volume":"126","author":"D Wang","year":"2020","unstructured":"Wang D, Wang P, Yuan Y, Wang P, Shi J (2020) A fast conformal predictive system with regularized extreme learning machine. Neural Netw 126:347\u2013361. https:\/\/doi.org\/10.1016\/j.neunet.2020.03.022","journal-title":"Neural Netw"},{"key":"3029_CR20","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785\u2013794. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"3029_CR21","doi-asserted-by":"publisher","unstructured":"Ferreira AJ, Figueiredo MA (2012) Boosting algorithms: a review of methods, theory, and applications. Ensemble Mach Learn 35\u201385. https:\/\/doi.org\/10.1007\/978-1-4419-9326-7_2","DOI":"10.1007\/978-1-4419-9326-7_2"},{"issue":"6","key":"3029_CR22","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/TCBB.2019.2911071","volume":"17","author":"A Ogunleye","year":"2019","unstructured":"Ogunleye A, Wang QG (2019) Xgboost model for chronic kidney disease diagnosis. IEEE\/ACM Trans Comput Biol Bioinf 17(6):2131\u20132140. https:\/\/doi.org\/10.1109\/TCBB.2019.2911071","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"4","key":"3029_CR23","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s41666-020-00077-1","volume":"4","author":"X Zhang","year":"2020","unstructured":"Zhang X, Yan C, Gao C, Malin BA, Chen Y (2020) Predicting missing values in medical data via XGBoost regression. J Healthcare Inform Res 4(4):383\u2013394. https:\/\/doi.org\/10.1007\/s41666-020-00077-1","journal-title":"J Healthcare Inform Res"},{"issue":"9","key":"3029_CR24","doi-asserted-by":"publisher","first-page":"e30770","DOI":"10.2196\/30770","volume":"9","author":"H Yun","year":"2021","unstructured":"Yun H, Choi J, Park JH et al (2021) Prediction of critical care outcome for adult patients presenting to emergency department using initial triage information: an XGBoost algorithm analysis. JMIR Med Inform 9(9):e30770. https:\/\/doi.org\/10.2196\/30770","journal-title":"JMIR Med Inform"},{"issue":"7","key":"3029_CR25","doi-asserted-by":"publisher","first-page":"e056685","DOI":"10.1136\/bmjopen-2021-056685","volume":"12","author":"F Zg","year":"2022","unstructured":"Zg F, Sq Y, Cx Lv, An Sy WuW (2022) Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study. BMJ Open 12(7):e056685. https:\/\/doi.org\/10.1136\/bmjopen-2021-056685","journal-title":"BMJ Open"},{"key":"3029_CR26","doi-asserted-by":"publisher","unstructured":"Papadopoulos H (2008) Inductive conformal prediction: theory and application to neural networks. In: Tools in artificial intelligence, Citeseer. https:\/\/doi.org\/10.5772\/6078","DOI":"10.5772\/6078"},{"issue":"8","key":"3029_CR27","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1016\/j.neunet.2011.05.008","volume":"24","author":"H Papadopoulos","year":"2011","unstructured":"Papadopoulos H, Haralambous H (2011) Reliable prediction intervals with regression neural networks. Neural Netw 24(8):842\u2013851. https:\/\/doi.org\/10.1016\/j.neunet.2011.05.008","journal-title":"Neural Netw"},{"issue":"1\u20132","key":"3029_CR28","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s10472-013-9368-4","volume":"74","author":"V Vovk","year":"2015","unstructured":"Vovk V (2015) Cross-conformal predictors. Ann Math Artif Intell 74(1\u20132):9\u201328. https:\/\/doi.org\/10.1007\/s10472-013-9368-4","journal-title":"Ann Math Artif Intell"},{"issue":"523","key":"3029_CR29","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1080\/01621459.2017.1307116","volume":"113","author":"J Lei","year":"2018","unstructured":"Lei J, G\u2019Sell M, Rinaldo A, Tibshirani RJ, Wasserman L (2018) Distribution-free predictive inference for regression. J Am Stat Assoc 113(523):1094\u20131111. https:\/\/doi.org\/10.1080\/01621459.2017.1307116","journal-title":"J Am Stat Assoc"},{"issue":"48","key":"3029_CR30","doi-asserted-by":"publisher","first-page":"e2107794118","DOI":"10.1073\/pnas.2107794118","volume":"118","author":"V Chernozhukov","year":"2021","unstructured":"Chernozhukov V, Wuthrich K, Zhu Y (2021) Distributional conformal prediction. Proc Natl Acad Sci 118(48):e2107794118. https:\/\/doi.org\/10.1073\/pnas.2107794118","journal-title":"Proc Natl Acad Sci"},{"key":"3029_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3139930","volume":"60","author":"P Wang","year":"2021","unstructured":"Wang P, Wang P, Wang D, Xue B (2021) A conformal regressor with random forests for tropical cyclone intensity estimation. IEEE Trans Geosci Remote Sens 60:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2021.3139930","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3029_CR32","doi-asserted-by":"crossref","unstructured":"Sunnetci KM, Alkan A (2022) Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images. Expert Systems with Applications p 119430","DOI":"10.1016\/j.eswa.2022.119430"},{"key":"3029_CR33","doi-asserted-by":"publisher","first-page":"104268","DOI":"10.1016\/j.bspc.2022.104268","volume":"80","author":"K Gupta","year":"2023","unstructured":"Gupta K, Bajaj V (2023) Deep learning models-based CT-scan image classification for automated screening of COVID-19. Biomed Signal Process Control 80:104268","journal-title":"Biomed Signal Process Control"},{"key":"3029_CR34","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1007\/s11517-023-02803-4","volume":"61","author":"S Ulukaya","year":"2023","unstructured":"Ulukaya S, Sar\u0131ca AA, Erdem O, Karaali A (2023) MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds. Med Biol Eng Comput 61:1619\u20131629. https:\/\/doi.org\/10.1007\/s11517-023-02803-4","journal-title":"Med Biol Eng Comput"},{"key":"3029_CR35","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2015.12.114","volume":"192","author":"A De Myttenaere","year":"2016","unstructured":"De Myttenaere A, Golden B, Le Grand B, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing 192:38\u201348","journal-title":"Neurocomputing"},{"issue":"1","key":"3029_CR36","first-page":"1525","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE). Geosci Model Dev Dis 7(1):1525\u20131534","journal-title":"Geosci Model Dev Dis"},{"issue":"6","key":"3029_CR37","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1111\/j.2005.0906-7590.04112.x","volume":"28","author":"BA Walther","year":"2005","unstructured":"Walther BA, Moore JL (2005) The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography 28(6):815\u2013829","journal-title":"Ecography"},{"issue":"6","key":"3029_CR38","doi-asserted-by":"publisher","first-page":"717","DOI":"10.3906\/tar-2007-105","volume":"45","author":"A Alkan","year":"2021","unstructured":"Alkan A, Abdullah MU, Abdullah HO, Assaf M, Zhou H (2021) A smart agricultural application: automated detection of diseases in vine leaves usinghybrid deep learning. Turkish J Agric Forest 45(6):717\u2013729","journal-title":"Turkish J Agric Forest"},{"key":"3029_CR39","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"3029_CR40","doi-asserted-by":"publisher","first-page":"5714714","DOI":"10.1155\/2020\/5714714","volume":"2020","author":"Z Car","year":"2020","unstructured":"Car Z, Baressi \u0160egota S, An\u0111eli\u0107 N, Lorencin I, Mrzljak V et al (2020) Modeling the spread of COVID-19 infection using a multilayer perceptron. Comput Math Methods Med 2020:5714714. https:\/\/doi.org\/10.1155\/2020\/5714714","journal-title":"Comput Math Methods Med"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03029-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03029-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03029-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T18:50:38Z","timestamp":1757098238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03029-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":40,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["3029"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03029-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2024,2,16]]},"assertion":[{"value":"14 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","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"}}]}}