Brodie D, Slutsky AS, Combes A. Extracorporeal Life support for adults with respiratory failure and related indications: a review. JAMA. 2019;322(6):557–68.

Article  PubMed  Google Scholar 

Makdisi G, Wang IW. Extra corporeal membrane oxygenation (ECMO) review of a lifesaving technology. J Thorac Dis. 2015;7(7):E166-176.

PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed  Google Scholar 

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):131–43.

Article  PubMed  Google Scholar 

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):1648–56.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

O’Neil ER, Guner Y, Anders MM, Priest J, Friedman ML, Raman L, Sandhu HS. Pediatric highlights from the extracorporeal life support organization registry: 2017–2022. ASAIO J. 2024;70(1):8–13.

Article  PubMed  Google Scholar 

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):1061–74.

Article  PubMed  Google Scholar 

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;39:1594–601.

Article  CAS  PubMed  Google Scholar 

(ELSO) ELSO: ELSO Registry Data Definitions; 2023.

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):2817–24.

Article  PubMed  Google Scholar 

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):338–44.

Article  PubMed  Google Scholar 

Sutter R, Tisljar K, Marsch S. Acute neurologic complications during extracorporeal membrane oxygenation: a systematic review. Crit Care Med. 2018;46(9):1506–13.

Article  CAS  PubMed  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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):e897–905.

Article  CAS  PubMed  PubMed Central  Google Scholar 

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):895–900.

Article  PubMed  Google Scholar 

Stekhoven DJ, Bühlmann P. MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics (Oxford, England). 2012;28(1):112–8.

CAS  PubMed  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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):40–9.

Article  PubMed  PubMed Central  Google Scholar 

Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N. A survey on addressing high-class imbalance in big data. J Big Data. 2018;5(1):1–30.

Article  Google Scholar 

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. 1322–1328

Tomek I. TWO MODIFICATIONS OF CNN. IEEE Trans Syst Man Cybernetics. 1976;SMC-6(11):769–72.

Article  Google Scholar 

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. 225–228

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Arti Intell Res. 2002;16:321–57.

Google Scholar 

Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Lautenbach S. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36(1):27–46.

Article  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed  Google Scholar 

Perry J, Brody JA, Fong C, Sunshine JE, O’Reilly-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.

Article  PubMed  Google Scholar 

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):155–68.

Article  PubMed  PubMed Central  Google Scholar 

Schölkopf B, Luo Z, Vovk V, editors. Empirical inference: Festschrift in honor of Vladimir N. Vapnik. Springer Science & Business Media; 2013

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):1113–22.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cawley GC, Talbot NLC. on over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res. 2010;11:2079–107.

Google Scholar 

Archer KJ, Kirnes RV. Empirical characterization of random forest variable importance measures. Comput Stat Data Anal. 2008;52(4):2249–60.

Article  Google Scholar 

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’s disease in primary care. EClinicalMedicine. 2024;76: 102834.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed