{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T21:28:01Z","timestamp":1790803681946,"version":"4.1.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82201905"],"award-info":[{"award-number":["82201905"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["81971433"],"award-info":[{"award-number":["81971433"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2021YFC2701700"],"award-info":[{"award-number":["2021YFC2701700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fundamental Research Funds for the Central University","award":["SCU2023D006"],"award-info":[{"award-number":["SCU2023D006"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Crit Care"],"DOI":"10.1186\/s13054-024-05248-9","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T04:26:38Z","timestamp":1736396798000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Development and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation"],"prefix":"10.1186","volume":"29","author":[{"given":"Bixin","family":"Deng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiechao","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang\u2019e","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzhe","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haixiu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongtao","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenting","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjie","family":"Ying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyang","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dezhi","family":"Mu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"issue":"6","key":"5248_CR1","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1001\/jama.2019.9302","volume":"322","author":"D Brodie","year":"2019","unstructured":"Brodie D, Slutsky AS, Combes A. Extracorporeal Life support for adults with respiratory failure and related indications: a review. JAMA. 2019;322(6):557\u201368.","journal-title":"JAMA"},{"issue":"7","key":"5248_CR2","first-page":"E166","volume":"7","author":"G Makdisi","year":"2015","unstructured":"Makdisi G, Wang IW. Extra corporeal membrane oxygenation (ECMO) review of a lifesaving technology. J Thorac Dis. 2015;7(7):E166-176.","journal-title":"J Thorac Dis"},{"issue":"9","key":"5248_CR3","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCHEARTFAILURE.118.004905","volume":"11","author":"P Rao","year":"2018","unstructured":"Rao P, Khalpey Z, Smith R, Burkhoff D, Kociol RD. Venoarterial extracorporeal membrane oxygenation for cardiogenic shock and cardiac arrest. Circ Heart Fail. 2018;11(9): e004905.","journal-title":"Circ Heart Fail"},{"issue":"2","key":"5248_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1097\/MAT.0000000000002128","volume":"70","author":"JE Tonna","year":"2024","unstructured":"Tonna JE, Boonstra PS, MacLaren G, Paden M, Brodie D, Anders M, Barbaro RP. extracorporeal life support organization registry international report 2022: 100,000 survivors. ASAIO J. 2024;70(2):131\u201343.","journal-title":"ASAIO J"},{"issue":"8","key":"5248_CR5","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1177\/02676591231212997","volume":"39","author":"J Deinzer","year":"2024","unstructured":"Deinzer J, Philipp A, Kmiec L, Li J, Wiesner S, Blecha S, Petermichl W, Lubnow M, Camboni D, Schmid C, et al. Mortality on extracorporeal membrane oxygenation: evaluation of independent risk factors and causes of death during venoarterial and venovenous support. Perfusion. 2024;39(8):1648\u201356.","journal-title":"Perfusion"},{"key":"5248_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.iccn.2023.103489","volume":"79","author":"Y Lee","year":"2023","unstructured":"Lee Y, Jang I, Hong J, Son YJ. Factors associated with 30-day in-hospital mortality in critically ill adult patients receiving extracorporeal membrane oxygenation: a retrospective cohort study. Intensive Crit Care Nurs. 2023;79: 103489.","journal-title":"Intensive Crit Care Nurs"},{"issue":"1","key":"5248_CR7","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1097\/MAT.0000000000002078","volume":"70","author":"ER O\u2019Neil","year":"2024","unstructured":"O\u2019Neil ER, Guner Y, Anders MM, Priest J, Friedman ML, Raman L, Sandhu HS. Pediatric highlights from the extracorporeal life support organization registry: 2017\u20132022. ASAIO J. 2024;70(1):8\u201313.","journal-title":"ASAIO J"},{"issue":"10","key":"5248_CR8","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.23736\/S0375-9393.17.11911-5","volume":"83","author":"R Lorusso","year":"2017","unstructured":"Lorusso R, Taccone FS, Belliato M, Delnoij T, Zanatta P, Cvetkovic M, Davidson M, Belohlavek J, Matta N, Davis C, et al. Brain monitoring in adult and pediatric ECMO patients: the importance of early and late assessments. Minerva Anestesiol. 2017;83(10):1061\u201374.","journal-title":"Minerva Anestesiol"},{"key":"5248_CR9","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1007\/s00134-013-2985-x","volume":"39","author":"A Polito","year":"2013","unstructured":"Polito A, Barrett CS, Wypij D, Rycus PT, Netto R, Cogo PE, Thiagarajan RR. Neurologic complications in neonates supported with extracorporeal membrane oxygenation. An analysis of ELSO registry data. Intensive care med. 2013;39:1594\u2013601.","journal-title":"Intensive care med"},{"key":"5248_CR10","unstructured":"(ELSO) ELSO: ELSO Registry Data Definitions; 2023."},{"issue":"12","key":"5248_CR11","doi-asserted-by":"publisher","first-page":"2817","DOI":"10.1097\/01.CCM.0000189940.70617.C3","volume":"33","author":"P Cengiz","year":"2005","unstructured":"Cengiz P, Seidel K, Rycus PT, Brogan TV, Roberts JS. Central nervous system complications during pediatric extracorporeal life support: incidence and risk factors. Crit Care Med. 2005;33(12):2817\u201324.","journal-title":"Crit Care Med"},{"issue":"4","key":"5248_CR12","doi-asserted-by":"publisher","first-page":"338","DOI":"10.3171\/2011.1.PEDS10443","volume":"7","author":"SL Hervey-Jumper","year":"2011","unstructured":"Hervey-Jumper SL, Annich GM, Yancon AR, Garton HJ, Muraszko KM, Maher CO. Neurological complications of extracorporeal membrane oxygenation in children. J Neurosurg Pediatr. 2011;7(4):338\u201344.","journal-title":"J Neurosurg Pediatr"},{"issue":"9","key":"5248_CR13","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1097\/CCM.0000000000003223","volume":"46","author":"R Sutter","year":"2018","unstructured":"Sutter R, Tisljar K, Marsch S. Acute neurologic complications during extracorporeal membrane oxygenation: a systematic review. Crit Care Med. 2018;46(9):1506\u201313.","journal-title":"Crit Care Med"},{"issue":"1","key":"5248_CR14","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1186\/s13054-024-05082-z","volume":"28","author":"SM Cho","year":"2024","unstructured":"Cho SM, Hwang J, Chiarini G, Amer M, Antonini MV, Barrett N, Belohlavek J, Brodie D, Dalton HJ, Diaz R, et al. Neurological monitoring and management for adult extracorporeal membrane oxygenation patients: extracorporeal Life Support Organization consensus guidelines. Crit Care. 2024;28(1):296.","journal-title":"Crit Care"},{"key":"5248_CR15","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385: e078378.","journal-title":"BMJ"},{"issue":"10","key":"5248_CR16","doi-asserted-by":"publisher","first-page":"e897","DOI":"10.1097\/CCM.0000000000004498","volume":"48","author":"SM Cho","year":"2020","unstructured":"Cho SM, Canner J, Chiarini G, Calligy K, Caturegli G, Rycus P, Barbaro RP, Tonna J, Lorusso R, Kilic A, et al. modifiable risk factors and mortality from ischemic and hemorrhagic strokes in patients receiving venoarterial extracorporeal membrane oxygenation: results from the extracorporeal life support organization registry. Crit Care Med. 2020;48(10):e897\u2013905.","journal-title":"Crit Care Med"},{"issue":"10","key":"5248_CR17","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1097\/MAT.0000000000001896","volume":"69","author":"P Pandiyan","year":"2023","unstructured":"Pandiyan P, Cvetkovic M, Antonini MV, Shappley RK, Karmakar SA, Raman L. clinical guidelines for routine neuromonitoring in neonatal and pediatric patients supported on extracorporeal membrane oxygenation. ASAIO J. 2023;69(10):895\u2013900.","journal-title":"ASAIO J"},{"issue":"1","key":"5248_CR18","first-page":"112","volume":"28","author":"DJ Stekhoven","year":"2012","unstructured":"Stekhoven DJ, B\u00fchlmann P. MissForest\u2013non-parametric missing value imputation for mixed-type data. Bioinformatics (Oxford, England). 2012;28(1):112\u20138.","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"1","key":"5248_CR19","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1186\/s12874-020-01080-1","volume":"20","author":"S Hong","year":"2020","unstructured":"Hong S, Lynn HS. Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction. BMC Med Res Methodol. 2020;20(1):199.","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"5248_CR20","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1002\/mpr.329","volume":"20","author":"MJ Azur","year":"2011","unstructured":"Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: What is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40\u20139.","journal-title":"Int J Methods Psychiatr Res"},{"issue":"1","key":"5248_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N. A survey on addressing high-class imbalance in big data. J Big Data. 2018;5(1):1\u201330.","journal-title":"J Big Data"},{"key":"5248_CR22","doi-asserted-by":"crossref","unstructured":"He H, Bai Y, Garcia EA, Li S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) 2008. pp. 1322\u20131328","DOI":"10.1109\/IJCNN.2008.4633969"},{"issue":"11","key":"5248_CR23","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1109\/TSMC.1976.4309452","volume":"SMC-6","author":"I Tomek","year":"1976","unstructured":"Tomek I. TWO MODIFICATIONS OF CNN. IEEE Trans Syst Man Cybernetics. 1976;SMC-6(11):769\u201372.","journal-title":"IEEE Trans Syst Man Cybernetics"},{"key":"5248_CR24","doi-asserted-by":"crossref","unstructured":"Zeng M, Zou B, Wei F, Liu X, Wang L. Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data. In: 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS) 2016. pp. 225\u2013228","DOI":"10.1109\/ICOACS.2016.7563084"},{"key":"5248_CR25","first-page":"321","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Arti Intell Res. 2002;16:321\u201357.","journal-title":"J Arti Intell Res"},{"issue":"1","key":"5248_CR26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/j.1600-0587.2012.07348.x","volume":"36","author":"CF Dormann","year":"2013","unstructured":"Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carr\u00e9 G, Lautenbach S. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36(1):27\u201346.","journal-title":"Ecography"},{"issue":"5","key":"5248_CR27","doi-asserted-by":"publisher","first-page":"afae101","DOI":"10.1093\/ageing\/afae101","volume":"53","author":"S Benovic","year":"2024","unstructured":"Benovic S, Ajlani AH, Leinert C, Fotteler M, Wolf D, Steger F, Kocar TD. Introducing a machine learning algorithm for delirium prediction: the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead). Age Ageing. 2024;53(5):afae101.","journal-title":"Age Ageing"},{"issue":"12","key":"5248_CR28","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.49111","volume":"6","author":"AK Bowe","year":"2023","unstructured":"Bowe AK, Lightbody G, Staines A, Murray DM, Norman M. Prediction of 2-year cognitive outcomes in very preterm infants using machine learning methods. JAMA Netw Open. 2023;6(12): e2349111.","journal-title":"JAMA Netw Open"},{"issue":"3","key":"5248_CR29","doi-asserted-by":"publisher","first-page":"afae045","DOI":"10.1093\/ageing\/afae045","volume":"53","author":"W Huang","year":"2024","unstructured":"Huang W, Wang J, Xu J, Guo G, Chen Z, Xue H. Multivariable machine learning models for clinical prediction of subsequent hip fractures in older people using the Chinese population database. Age Ageing. 2024;53(3):afae045.","journal-title":"Age Ageing"},{"key":"5248_CR30","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.124.069105","author":"J Perry","year":"2024","unstructured":"Perry J, Brody JA, Fong C, Sunshine JE, O\u2019Reilly-Shah VN, Sayre MR, Chatterjee NA. Predicting out-of-hospital cardiac arrest in the general population using electronic health records. Circulation. 2024. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.124.069105.","journal-title":"Circulation"},{"issue":"1","key":"5248_CR31","doi-asserted-by":"publisher","first-page":"155","DOI":"10.62347\/LNDL8700","volume":"14","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Ma Y, Wang J, Guan Q, Yu B. Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning. Am J Cancer Res. 2024;14(1):155\u201368.","journal-title":"Am J Cancer Res"},{"key":"5248_CR32","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf B, Luo Z, Vovk V, editors. Empirical inference: Festschrift in honor of Vladimir N. Vapnik. Springer Science & Business Media; 2013","DOI":"10.1007\/978-3-642-41136-6"},{"issue":"5","key":"5248_CR33","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/s41591-023-02332-5","volume":"29","author":"D Placido","year":"2023","unstructured":"Placido D, Yuan B, Hjaltelin JX, Zheng C, Haue AD, Chmura PJ, Yuan C, Kim J, Umeton R, Antell G, et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med. 2023;29(5):1113\u201322.","journal-title":"Nat Med"},{"key":"5248_CR34","first-page":"2079","volume":"11","author":"GC Cawley","year":"2010","unstructured":"Cawley GC, Talbot NLC. on over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res. 2010;11:2079\u2013107.","journal-title":"J Mach Learn Res"},{"issue":"4","key":"5248_CR35","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","volume":"52","author":"KJ Archer","year":"2008","unstructured":"Archer KJ, Kirnes RV. Empirical characterization of random forest variable importance measures. Comput Stat Data Anal. 2008;52(4):2249\u201360.","journal-title":"Comput Stat Data Anal"},{"key":"5248_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2024.102834","volume":"76","author":"Y Pan","year":"2024","unstructured":"Pan Y, Chu C, Wang Y, Wang Y, Ji G, Masters CL, Goudey B, Jin L. Development and validation of the florey dementia risk score web-based tool to screen for Alzheimer\u2019s disease in primary care. EClinicalMedicine. 2024;76: 102834.","journal-title":"EClinicalMedicine"},{"issue":"3","key":"5248_CR37","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.2240","volume":"4","author":"B Xue","year":"2021","unstructured":"Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, Kannampallil T, Abraham J. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open. 2021;4(3): e212240.","journal-title":"JAMA Netw Open"},{"key":"5248_CR38","doi-asserted-by":"publisher","first-page":"4163","DOI":"10.2147\/JIR.S464489","volume":"17","author":"Z Zhang","year":"2024","unstructured":"Zhang Z, Shao B, Liu H, Huang B, Gao X, Qiu J, Wang C. Construction and validation of a predictive model for coronary artery disease using extreme gradient boosting. J Inflamm Res. 2024;17:4163\u201374.","journal-title":"J Inflamm Res"},{"key":"5248_CR39","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s12874-015-0015-0","volume":"15","author":"SL Chong","year":"2015","unstructured":"Chong SL, Liu N, Barbier S, Ong ME. Predictive modeling in pediatric traumatic brain injury using machine learning. BMC Med Res Methodol. 2015;15:22.","journal-title":"BMC Med Res Methodol"},{"key":"5248_CR40","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.962027","volume":"9","author":"C Ge","year":"2022","unstructured":"Ge C, Deng F, Chen W, Ye Z, Zhang L, Ai Y, Zou Y, Peng Q. Machine learning for early prediction of sepsis-associated acute brain injury. Front Med (Lausanne). 2022;9: 962027.","journal-title":"Front Med (Lausanne)"},{"issue":"3","key":"5248_CR41","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1007\/s12028-021-01405-y","volume":"36","author":"A Mansour","year":"2022","unstructured":"Mansour A, Fuhrman JD, Ammar FE, Loggini A, Davis J, Lazaridis C, Kramer C, Goldenberg FD, Giger ML. Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest. Neurocrit Care. 2022;36(3):974\u201382.","journal-title":"Neurocrit Care"},{"issue":"9","key":"5248_CR42","doi-asserted-by":"publisher","first-page":"2718","DOI":"10.3390\/jcm9092718","volume":"9","author":"N Shah","year":"2020","unstructured":"Shah N, Farhat A, Tweed J, Wang Z, Lee J, McBeth R, Raman L. Neural networks to predict radiographic brain injury in pediatric patients treated with extracorporeal membrane oxygenation. J Clinic Med. 2020;9(9):2718.","journal-title":"J Clinic Med"},{"key":"5248_CR43","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.xjon.2024.06.001","volume":"20","author":"A Kalra","year":"2024","unstructured":"Kalra A, Bachina P, Shou BL, Hwang J, Barshay M, Kulkarni S, Sears I, Eickhoff C, Bermudez CA, Brodie D, et al. Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: an extracorporeal life support organization registry analysis. JTCVS Open. 2024;20:64\u201388.","journal-title":"JTCVS Open"},{"issue":"4","key":"5248_CR44","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1016\/j.athoracsur.2020.03.128","volume":"110","author":"B Ayers","year":"2020","unstructured":"Ayers B, Wood K, Gosev I, Prasad S. predicting survival after extracorporeal membrane oxygenation by using machine learning. Ann Thorac Surg. 2020;110(4):1193\u2013200.","journal-title":"Ann Thorac Surg"},{"issue":"9","key":"5248_CR45","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1007\/s00134-023-07157-x","volume":"49","author":"AF Stephens","year":"2023","unstructured":"Stephens AF, \u0160eman M, Diehl A, Pilcher D, Barbaro RP, Brodie D, Pellegrino V, Kaye DM, Gregory SD, Hodgson C. ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation. Intensive Care Med. 2023;49(9):1090\u20139.","journal-title":"Intensive Care Med"},{"issue":"1","key":"5248_CR46","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1186\/s13054-020-03403-6","volume":"24","author":"A Abbasi","year":"2020","unstructured":"Abbasi A, Karasu Y, Li C, Sodha NR, Eickhoff C, Ventetuolo CE. Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation. Crit Care. 2020;24(1):689.","journal-title":"Crit Care"},{"issue":"4","key":"5248_CR47","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.healun.2022.10.019","volume":"42","author":"BL Shou","year":"2023","unstructured":"Shou BL, Ong CS, Premraj L, Brown P, Tonna JE, Dalton HJ, Kim BS, Keller SP, Whitman GJR, Cho SM. Arterial oxygen and carbon dioxide tension and acute brain injury in extracorporeal cardiopulmonary resuscitation patients: analysis of the extracorporeal life support organization registry. J Heart Lung Transplant. 2023;42(4):503\u201311.","journal-title":"J Heart Lung Transplant"},{"issue":"6","key":"5248_CR48","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1016\/j.athoracsur.2023.09.019","volume":"117","author":"A Kalra","year":"2024","unstructured":"Kalra A, Shou BL, Zhao D, Wilcox C, Keller SP, Kim BS, Whitman GJR, Cho SM. extracorporeal membrane oxygenation physiological factors influence pulse oximetry and arterial oxygen saturation discrepancies. Ann Thorac Surg. 2024;117(6):1221\u20138.","journal-title":"Ann Thorac Surg"},{"issue":"4","key":"5248_CR49","doi-asserted-by":"publisher","first-page":"e299","DOI":"10.1097\/CCM.0000000000004242","volume":"48","author":"J Wollborn","year":"2020","unstructured":"Wollborn J, Steiger C, Doostkam S, Schallner N, Schroeter N, Kari FA, Meinel L, Buerkle H, Schick MA, Goebel U. Carbon monoxide exerts functional neuroprotection after cardiac arrest using extracorporeal resuscitation in pigs. Crit Care Med. 2020;48(4):e299\u2013307.","journal-title":"Crit Care Med"},{"issue":"1","key":"5248_CR50","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1097\/CCM.0000000000004707","volume":"49","author":"SM Cho","year":"2021","unstructured":"Cho SM, Canner J, Caturegli G, Choi CW, Etchill E, Giuliano K, Chiarini G, Calligy K, Rycus P, Lorusso R, et al. Risk factors of ischemic and hemorrhagic strokes during venovenous extracorporeal membrane oxygenation: analysis of data from the extracorporeal life support organization registry. Crit Care Med. 2021;49(1):91\u2013101.","journal-title":"Crit Care Med"},{"issue":"4","key":"5248_CR51","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1055\/s-0041-1726284","volume":"41","author":"G Chiarini","year":"2021","unstructured":"Chiarini G, Cho SM, Whitman G, Rasulo F, Lorusso R. Brain injury in extracorporeal membrane oxygenation: a multidisciplinary approach. Semin Neurol. 2021;41(4):422\u201336.","journal-title":"Semin Neurol"},{"issue":"1","key":"5248_CR52","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1097\/ALN.0000000000004980","volume":"141","author":"J Helms","year":"2024","unstructured":"Helms J, Curtiaud A, Severac F, Tschirhart M, Merdji H, Bourdin M, Contant G, Depasse F, Abou Rjeily R, Sattler L, et al. Fibrinolysis as a causative mechanism for bleeding complications on extracorporeal membrane oxygenation: a pilot observational prospective study. Anesthesiology. 2024;141(1):75\u201386.","journal-title":"Anesthesiology"},{"issue":"2","key":"5248_CR53","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1053\/j.semperi.2017.12.007","volume":"42","author":"N Lin","year":"2018","unstructured":"Lin N, Flibotte J, Licht DJ. Neuromonitoring in the neonatal ECMO patient. Semin Perinatol. 2018;42(2):111\u201321.","journal-title":"Semin Perinatol"}],"container-title":["Critical Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13054-024-05248-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13054-024-05248-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13054-024-05248-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T06:06:59Z","timestamp":1736402819000},"score":1,"resource":{"primary":{"URL":"https:\/\/ccforum.biomedcentral.com\/articles\/10.1186\/s13054-024-05248-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,9]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["5248"],"URL":"https:\/\/doi.org\/10.1186\/s13054-024-05248-9","relation":{},"ISSN":["1364-8535"],"issn-type":[{"value":"1364-8535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,9]]},"assertion":[{"value":"21 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2025","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 have no conflicts of interest relevant to this article to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17"}}