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Artificial Intelligence in Health                            Predicting ICU mortality: A stacked ensemble model



               doi: 10.1016/j.procs.2019.09.167                   2018;140:306-313.
            20.  Breiman L. Random forests. Mach Learn. 2001;45(1):5-32.     doi: 10.1016/j.procs.2018.10.313
               doi: 10.1023/A:1010933404324                    32.  Dan T, Li Y, Zhu Z, et al. Machine Learning to Predict ICU
                                                                  Admission, ICU Mortality and Survivors’ Length of Stay among
            21.  Geurts P, Ernst D, Wehenkel L. Extremely randomized trees.
               Mach Learn. 2006;63(1):3-42.                       COVID-19 Patients: Toward Optimal Allocation of ICU
                                                                  Resources. In: Proceedings-2020 IEEE International Conference
               doi: 10.1007/s10994-006-6226-1                     on Bioinformatics and Biomedicine, BIBM 2020; 2020.
            22.  Chen T, Guestrin C. XGBoost: A  Scalable Tree Boosting      doi: 10.1109/BIBM49941.2020.9313292
               System. In:  Proceedings of the 22   ACM SIGKDD
                                            nd
               International Conference on Knowledge Discovery and Data   33.  Yu L, Halalau A, Dalal B, et al. Machine learning methods
               Mining. KDD ’16. Association for Computing Machinery;   to predict mechanical ventilation and mortality in patients
               2016. p. 785-794.                                  with COVID-19. PLoS One. 2021;16(4):e0249285.
                                                                  doi: 10.1371/journal.pone.0249285
               doi: 10.1145/2939672.2939785
                                                               34.  Hwangbo L, Kang YJ, Kwon H,  et al. Stacking ensemble
            23.  Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin
               A. CatBoost: Unbiased boosting with categorical features.   learning model to predict 6-month mortality in ischemic
               Adv Neural Inf Process Syst. 2018;31: 2-8.         stroke patients. Sci Rep. 2022;12(1):17389.
                                                                  doi: 10.1038/s41598-022-22323-9
            24.  Ke G, Meng Q, Finley T, et al. LightGBM: A highly efficient
               gradient boosting decision tree. In:  Advances in Neural   35.  Iwase S, Nakada TA, Shimada T, et al. Prediction algorithm
               Information Processing Systems. Vol. 2017. United States: The   for ICU mortality and length of stay using machine learning.
               MIT Press; 2017.                                   Sci Rep. 2022;12(1):12912.

            25.  McCulloch WS, Pitts W. A  logical calculus of the ideas      doi: 10.1038/s41598-022-17091-5
               immanent in nervous activity: The bulletin of mathematical   36.  Pang K, Li L, Ouyang W, Liu X, Tang Y. Establishment of ICU
               biophysics. Bull Math Biophys. 1943;5(4):115-133.
                                                                  mortality risk prediction models with machine learning algorithm
               doi: 10.1007/BF02478259                            using MIMIC-IV database. Diagnostics. 2022;12(5):1068.
            26.  Caruana R, Niculescu-Mizil A, Crew G, Ksikes A. Ensemble      doi: 10.3390/diagnostics12051068
               selection from libraries of models. In:  Proceedings of the   37.  Saadatmand S, Salimifard K, Mohammadi R, Kuiper   A,
               Twenty-First International Conference on Machine Learning;   Marzban M, Farhadi A. Using machine learning in
               2004.
                                                                  prediction of ICU admission, mortality, and length of stay
               doi: 10.1145/1015330.1015432                       in the early stage of admission of COVID-19 patients. Ann
                                                                  Oper Res. 2023;328(1):1-29.
            27.  Alshari H, Abdulrazak Yahya S, Odabaş A. Comparison
               of gradient boosting decision tree algorithms for CPU      doi: 10.1007/s10479-022-04984-x
               performance. Erciyes Univ J Inst Sci Technol. 2021;37(1):157-168.
                                                               38.  Sun Y, He Z, Ren J, Wu Y. Prediction model of in-hospital
            28.  Anghel A, Papandreou N, Parnell T, De Palma A,   mortality in intensive care unit patients with cardiac arrest:
               Pozidis  H.  Benchmarking and Optimization of Gradient   A  retrospective  analysis  of  MIMIC-IV  database  based  on
               Boosting Decision Tree Algorithms. [arXiv Preprint].  machine learning. BMC Anesthesiol. 2023;23(1):178.
               doi: 10.48550/arXiv.1809.04559                     doi: 10.1186/s12871-023-02138-5
            29.  Wolpert DH.  Stacked generalization.  Neural Netw.   39.  Churpek MM, Snyder A, Han X, et al. Quick sepsis-related
               1992;5(2):241-259.                                 organ failure assessment, systemic inflammatory response
                                                                  syndrome, and early warning scores for detecting clinical
               doi: 10.1016/S0893-6080(05)80023-1
                                                                  deterioration in infected patients outside the intensive care
            30.  Viton F, Elbattah M, Guérin JL, Dequen G. Multi-channel   unit. Am J Respir Crit Care Med. 2017;195(7):906-911.
               ConvNet approach to predict the risk of in-hospital mortality
               for ICU patients. In:  Proceedings of the 1   International      doi: 10.1164/rccm.201604-0854OC
                                               st
               Conference  on  Deep  Learning  Theory  and  Applications.   40.  Liu  X,  Niu  H,  Peng  J.  Enhancing  predictions  with  a
               Portugal:  SCITEPRESS-Science  and  Technology     stacking ensemble model for ICU mortality risk in patients
               Publications; 2020. p. 98-102.                     with  sepsis-associated  encephalopathy.  J  Int  Med Res.
                                                                  2024;52(3):03000605241239013.
               doi: 10.5220/0009891900980102
                                                                  doi: 10.1177/03000605241239013
            31.  Darabi HR, Tsinis D, Zecchini K, Whitcomb WF, Liss A.
               Forecasting mortality risk for patients admitted to intensive   41.  Freijeiro-González L, Febrero-Bande M, González-
               care units using machine learning. In: Procedia Comput Sci.   Manteiga  W. A critical review of LASSO and its derivatives


            Volume 2 Issue 2 (2025)                         58                               doi: 10.36922/aih.4981
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