{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T12:05:28Z","timestamp":1775563528799,"version":"3.50.1"},"reference-count":24,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":62,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.03.078","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:39:40Z","timestamp":1774355980000},"page":"1013-1020","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["An Ensemble Learning Approach for Predicting Acute Respiratory Distress Syndrome Within Polytraumatic Patients In Imbalanced Dataset"],"prefix":"10.1016","volume":"278","author":[{"given":"Nesrine Ben","family":"El Hadj Hassine","sequence":"first","affiliation":[]},{"given":"Chamseddine","family":"Barki","sequence":"additional","affiliation":[]},{"given":"Hanene Boussi","family":"Rahmouni","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.procs.2026.03.078_bib1","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.surg.2006.06.015","article-title":"Decreased progression of postinjury lung dysfunction to the acute respiratory distress syndrome and multiple organ failure","volume":"140","author":"Ciesla","year":"2006","journal-title":"Surgery"},{"issue":"7","key":"10.1016\/j.procs.2026.03.078_bib2","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1001\/archsurg.141.7.655","article-title":"Acute respiratory distress syndrome in the trauma intensive care unit: Morbid but not mortal","volume":"141","author":"Salim","year":"2006","journal-title":"Arch Surg"},{"issue":"6","key":"10.1016\/j.procs.2026.03.078_bib3","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1177\/000313481808400623","article-title":"Mild to moderate to severe: What drives the severity of ards in trauma patients?","volume":"84","author":"Daher","year":"2018","journal-title":"J The American Surgeon"},{"issue":"6","key":"10.1016\/j.procs.2026.03.078_bib4","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.medj.2021.04.006","article-title":"Machine learning in clinical decision making","volume":"2","author":"Adlung","year":"2021","journal-title":"Med"},{"key":"10.1016\/j.procs.2026.03.078_bib5","doi-asserted-by":"crossref","unstructured":"Das, S., S. P. Nayak, B. Sahoo, and S. C. Nayak. Evaluating ensemble models on imbalanced data sets: A comparative study across varied minority class ratios. in International Conference on Emerging Systems and Intelligent Computing (ESIC). 2024.","DOI":"10.1109\/ESIC60604.2024.10481583"},{"key":"10.1016\/j.procs.2026.03.078_bib6","doi-asserted-by":"crossref","first-page":"109960","DOI":"10.1109\/ACCESS.2021.3102399","article-title":"A comparative performance analysis of data resampling methods on imbalance medical data","volume":"9","author":"Khushi","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.procs.2026.03.078_bib7","doi-asserted-by":"crossref","unstructured":"Bhattarai, S., A. Gupta, E. Ali, M. Ali, M. Riad, P. Adhikari, and J. A. Mostafa, Can big data and machine learning improve our understanding of acute respiratory distress syndrome? Cureus, 2021. 13(2).","DOI":"10.7759\/cureus.13529"},{"key":"10.1016\/j.procs.2026.03.078_bib8","doi-asserted-by":"crossref","first-page":"1050849","DOI":"10.3389\/fphys.2022.1050849","article-title":"Using machine learning for the early prediction of sepsis-associated ards in the icu and identification of clinical phenotypes with differential responses to treatment","volume":"13","author":"Bai","year":"2022","journal-title":"Front Physiol"},{"issue":"1","key":"10.1016\/j.procs.2026.03.078_bib9","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1186\/s12967-019-2075-0","article-title":"Predictive model for acute respiratory distress syndrome events in icu patients in china using machine learning algorithms: A secondary analysis of a cohort study","volume":"17","author":"Ding","year":"2019","journal-title":"J Transl Med"},{"issue":"1","key":"10.1016\/j.procs.2026.03.078_bib10","first-page":"e0313","article-title":"Use of machine learning to screen for acute respiratory distress syndrome using raw ventilator waveform data","volume":"3","author":"Rehm","year":"2021","journal-title":"Crit Care Explor"},{"issue":"2","key":"10.1016\/j.procs.2026.03.078_bib11","doi-asserted-by":"crossref","first-page":"e0226962","DOI":"10.1371\/journal.pone.0226962","article-title":"A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters","volume":"15","author":"Yang","year":"2020","journal-title":"PLOS ONE"},{"issue":"3","key":"10.1016\/j.procs.2026.03.078_bib12","doi-asserted-by":"crossref","first-page":"e0214465","DOI":"10.1371\/journal.pone.0214465","article-title":"Machine learning for patient risk stratification for acute respiratory distress syndrome","volume":"14","author":"Zeiberg","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.procs.2026.03.078_bib13","doi-asserted-by":"crossref","first-page":"100215","DOI":"10.1016\/j.clinsp.2023.100215","article-title":"Early prediction of acute respiratory distress syndrome complicated by acute pancreatitis based on four machine learning models","volume":"78","author":"Zhang","year":"2023","journal-title":"Clinics (Sao Paulo)"},{"issue":"8","key":"10.1016\/j.procs.2026.03.078_bib14","doi-asserted-by":"crossref","first-page":"5443","DOI":"10.1007\/s00330-020-07635-6","article-title":"Radiomics score predicts acute respiratory distress syndrome based on the initial ct scan after trauma","volume":"31","author":"R\u00f6hrich","year":"2021","journal-title":"Eur Radiol"},{"issue":"4","key":"10.1016\/j.procs.2026.03.078_bib15","doi-asserted-by":"crossref","first-page":"e0213836","DOI":"10.1371\/journal.pone.0213836","article-title":"Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma","volume":"14","author":"Christie","year":"2019","journal-title":"PLoS One"},{"issue":"23","key":"10.1016\/j.procs.2026.03.078_bib16","first-page":"2526","article-title":"Acute respiratory distress syndrome: The berlin definition","volume":"307","author":"Ranieri","year":"2012","journal-title":"Jama"},{"issue":"1","key":"10.1016\/j.procs.2026.03.078_bib17","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s40537-025-01119-4","article-title":"Resampling approaches to handle class imbalance: A review from a data perspective","volume":"12","author":"Carvalho","year":"2025","journal-title":"Journal of Big Data"},{"key":"10.1016\/j.procs.2026.03.078_bib18","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"Smote: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of artificial intelligence research"},{"issue":"1","key":"10.1016\/j.procs.2026.03.078_bib19","first-page":"171","volume":"59","author":"Wang","year":"2023","journal-title":"Prediction of acute respiratory distress syndrome in traumatic brain injury patients based on machine learning algorithms."},{"key":"10.1016\/j.procs.2026.03.078_bib20","doi-asserted-by":"crossref","first-page":"757","DOI":"10.55248\/gengpi.5.0324.0629","article-title":"Ensemble learning approaches for improved predictive analytics in healthcare","volume":"5","author":"Venugopal","year":"2024","journal-title":"International Journal of Research Publication and Reviews"},{"issue":"1","key":"10.1016\/j.procs.2026.03.078_bib21","first-page":"34","article-title":"Value of procalcitonin on predicting the severity and prognosis in patients with early ards: A prospective observation study","volume":"29","author":"Yu","year":"2017","journal-title":"Zhonghua Wei Zhong Bing Ji Jiu Yi Xue"},{"issue":"4","key":"10.1016\/j.procs.2026.03.078_bib22","first-page":"240","article-title":"Influence of flail chest on outcome among patients with severe thoracic cage trauma","volume":"87","author":"Velmahos","year":"2002","journal-title":"Int Surg"},{"issue":"3","key":"10.1016\/j.procs.2026.03.078_bib23","first-page":"717","article-title":"The role of acute blood transfusion in the development of acute respiratory distress syndrome in patients with severe trauma","volume":"59","author":"Silverboard","year":"2005","journal-title":"J Journal of Trauma Acute Care Surgery"},{"key":"10.1016\/j.procs.2026.03.078_bib24","doi-asserted-by":"crossref","unstructured":"Brascia, D., G. De Iaco, A. De Palma, M. Costantino, M. Genualdo, R. Quercia, A. Fiorella, G. Nex, M. Schiavone, F. Signore, T. Panza, F. Rea, and G. Marulli, Surgical stabilization of flail chest after trauma: When, why and how to do it? J Current Challenges in Thoracic Surgery, 2020. 2.","DOI":"10.21037\/ccts.2020.02.10"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926006733?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926006733?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:24:58Z","timestamp":1775561098000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926006733"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":24,"alternative-id":["S1877050926006733"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.03.078","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An Ensemble Learning Approach for Predicting Acute Respiratory Distress Syndrome Within Polytraumatic Patients In Imbalanced Dataset","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.03.078","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}