{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T12:00:54Z","timestamp":1784635254079,"version":"3.55.0"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81970285"],"award-info":[{"award-number":["81970285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","internationaljournalofcardiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Cardiology"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.ijcard.2022.04.063","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T23:13:43Z","timestamp":1650928423000},"page":"59-64","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":21,"special_numbering":"C","title":["Prediction of hospital mortality in mechanically ventilated patients with congestive heart failure using machine learning approaches"],"prefix":"10.1016","volume":"358","author":[{"given":"Le","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulong","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shangyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijcard.2022.04.063_bb0005","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1016\/S0140-6736(20)30925-9","article-title":"Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019","volume":"396","author":"Diseases","year":"2020","journal-title":"Lancet."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0010","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/s41569-020-0379-7","article-title":"Congestion in heart failure: a contemporary look at physiology, diagnosis and treatment","volume":"17","author":"Boorsma","year":"2020","journal-title":"Nat. Rev. Cardiol."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0015","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1016\/j.jacc.2018.06.074","article-title":"Positive pressure ventilation in the cardiac intensive care unit","volume":"72","author":"Alviar","year":"2018","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0020","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1016\/j.mayocp.2017.05.004","article-title":"Mechanical ventilation: state of the art","volume":"92","author":"Pham","year":"2017","journal-title":"Mayo Clin. Proc."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0025","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/S2213-2600(15)00150-2","article-title":"Long-term survival of critically ill patients treated with prolonged mechanical ventilation: a systematic review and meta-analysis","volume":"3","author":"Damuth","year":"2015","journal-title":"Lancet Respir. Med."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0030","article-title":"Development and validation of a machine-learning model for prediction of Extubation failure in intensive care units","volume":"8","author":"Zhao","year":"2021","journal-title":"Front. Med. (Lausanne)"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105869","article-title":"A machine learning decision-making tool for extubation in intensive care unit patients","volume":"200","author":"Fabregat","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0040","article-title":"Machine learning prediction models for mechanically ventilated patients: analyses of the MIMIC-III database","volume":"8","author":"Zhu","year":"2021","journal-title":"Front. Med. (Lausanne)"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0045","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future - big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0050","series-title":"MIMIC-IV (version 1.0)","author":"Johnson","year":"2021"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0055","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2018.178","article-title":"The eICU collaborative research database, a freely available multi-center database for critical care research","volume":"5","author":"Pollard","year":"2018","journal-title":"Sci. Data"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0060","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0065","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.mcna.2018.12.008","article-title":"Community-acquired pneumonia and hospital-acquired pneumonia","volume":"103","author":"Lanks","year":"2019","journal-title":"Med. Clin. North Am."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0070","doi-asserted-by":"crossref","first-page":"c179","DOI":"10.1159\/000339789","article-title":"KDIGO clinical practice guidelines for acute kidney injury","volume":"120","author":"Khwaja","year":"2012","journal-title":"Nephron. Clin. Pract."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0075","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1080\/00273171.2019.1694477","article-title":"Sparse extended redundancy analysis: variable selection via the exclusive LASSO","volume":"56","author":"Kok","year":"2021","journal-title":"Multivar. Behav. Res."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0080","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0085","doi-asserted-by":"crossref","DOI":"10.3390\/s20164629","article-title":"Evaluation of hyperparameter optimization in machine and deep learning methods for decoding imagined speech EEG","volume":"20","author":"Cooney","year":"2020","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0090","first-page":"1","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","volume":"18","author":"Li","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0095","doi-asserted-by":"crossref","first-page":"e173","DOI":"10.1016\/S1470-2045(14)71116-7","article-title":"Nomograms in oncology: more than meets the eye","volume":"16","author":"Balachandran","year":"2015","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0100","first-page":"30","article-title":"Multiple imputation with multivariate imputation by chained equation (MICE) package","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann. Transl. Med."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0105","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: an interdisciplinary review","volume":"7","author":"Hancock","year":"2020","journal-title":"J. Big Data"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0110","series-title":"Advances in Neural Information Processing Systems 31","first-page":"6638","article-title":"CatBoost: unbiased boosting with categorical features","author":"Prokhorenkova","year":"2018"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0115","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jchf.2019.06.013","article-title":"Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction","volume":"8","author":"Angraal","year":"2020","journal-title":"JACC Heart Fail"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0120","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1002\/ehf2.12419","article-title":"Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics","volume":"6","author":"Awan","year":"2019","journal-title":"ESC Heart Fail"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0125","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1016\/j.jacc.2009.06.041","volume":"54","author":"Cleland","year":"2009","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0130","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1016\/j.jacc.2016.09.931","article-title":"Prognostic implications of changes in N-terminal pro-B-type natriuretic peptide in patients with heart failure","volume":"68","author":"Zile","year":"2016","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0135","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.ejim.2014.08.005","article-title":"Troponin T in acute heart failure: clinical implications and prognosis in the Spanish National Registry on Heart Failure","volume":"25","author":"Guisado Espartero","year":"2014","journal-title":"Eur. J. Intern. Med."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0140","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1161\/CIRCHEARTFAILURE.113.000450","article-title":"Prognostic effect of high-sensitive troponin T assessment in elderly patients with chronic heart failure: results from the CORONA trial","volume":"7","author":"Gravning","year":"2014","journal-title":"Circ. Heart Fail."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0145","article-title":"Association of oliguria with the development of acute kidney injury in the critically ill","author":"Vaara","year":"2015","journal-title":"Kidney Int."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0150","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ijcard.2017.07.056","article-title":"Risk of stroke in congestive heart failure with and without atrial fibrillation","volume":"248","author":"Kang","year":"2017","journal-title":"Int. J. Cardiol."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0155","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ccm.2004.10.013","article-title":"Pathophysiology of pneumonia","volume":"26","author":"Alcon","year":"2005","journal-title":"Clin. Chest Med."},{"key":"10.1016\/j.ijcard.2022.04.063_bb0160","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1186\/s13054-019-2663-7","article-title":"The SOFA score-development, utility and challenges of accurate assessment in clinical trials","volume":"23","author":"Lambden","year":"2019","journal-title":"Crit. Care"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0165","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1001\/jama.1996.03540100046027","article-title":"The logistic organ dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU Scoring Group","volume":"276","author":"Le Gall","year":"1996","journal-title":"JAMA"},{"key":"10.1016\/j.ijcard.2022.04.063_bb0170","first-page":"557","article-title":"ICU severity of illness scores: APACHE, SAPS and MPM","volume":"20","author":"Salluh","year":"2014","journal-title":"Curr. Opin. Crit. Care"}],"container-title":["International Journal of Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167527322005812?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167527322005812?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T20:57:46Z","timestamp":1759093066000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167527322005812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":34,"alternative-id":["S0167527322005812"],"URL":"https:\/\/doi.org\/10.1016\/j.ijcard.2022.04.063","relation":{},"ISSN":["0167-5273"],"issn-type":[{"value":"0167-5273","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Prediction of hospital mortality in mechanically ventilated patients with congestive heart failure using machine learning approaches","name":"articletitle","label":"Article Title"},{"value":"International Journal of Cardiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijcard.2022.04.063","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}