Comparison of machine learning algorithms to SAPS II in predicting in-hospital mortality of fractures of the pelvis and acetabulum: analyzes based on MIMIC-III database

References

• Alaa AM, Bolton T, Di Angelantonio E, Rudd JHF, van der Schaar M. 2019. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLOS ONE. 14(5):e0213653. doi:10.1371/journal.pone.0213653.
   PubMed Web of Science ®Google Scholar
• Badriyah T, Briggs JS, Meredith P, Jarvis SW, Schmidt PE, Featherstone PI, Prytherch DR, Smith GB. 2014. Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS). Resuscitation. 85(3):418–423. doi:10.1016/j.resuscitation.2013.12.011.
   PubMed Web of Science ®Google Scholar
• Cai Y. 2021. Mortality Prediction MIMIC-III. V1. Science Data Bank. 2021. doi:10.11922/sciencedb.00787.
   Google Scholar
• Celi L, Tang R, Villarroel M, Davidzon G, Lester W, Chueh H. 2011. A clinical database-driven approach to decision support: predicting mortality among patients with acute kidney injury. J Healthc Eng. 2(1):97–110. doi:10.1260/2040-2295.2.1.97.
   PubMed Web of Science ®Google Scholar
• Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. 2016. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med. 44(2):368–374. doi:10.1097/CCM.0000000000001571.
   PubMed Web of Science ®Google Scholar
• Dechert TA, Duane TM, Frykberg BP, Aboutanos MB, Malhotra AK, Ivatury RR. 2009. Elderly patients with pelvic fracture: interventions and outcomes. Am Surg. 75(4):291–295. doi:10.1177/000313480907500405.
   PubMed Web of Science ®Google Scholar
• Eftekhar B, Mohammad K, Ardebili HE, Ghodsi M, Ketabchi E. 2005. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med Inform Decis Mak. 5(3). doi:10.1186/1472-6947-5-3
   PubMedGoogle Scholar
• Fernandez-Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 15:49.
   Google Scholar
• Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinforma Oxf Engl. 16(10):906–914. doi:10.1093/bioinformatics/16.10.906.
   PubMed Web of Science ®Google Scholar
• Ghassemi M, Marshall J, Singh N, Stone DJ, Celi LA. 2014. Leveraging a critical care database. Chest. 145(4):745–752. doi:10.1378/chest.13-1722.
   PubMedGoogle Scholar
• GiViTI PD, Rossi C, et al. 2012. Comparison between SAPS II and SAPS 3 in predicting hospital mortality in a cohort of 103 Italian ICUs. Is new always better? Intensive Care Med. 38(8):1280–1288. doi:10.1007/s00134-012-2578-0.
   PubMed Web of Science ®Google Scholar
• Gurovich Y, Hanani Y, Bar O, et al. 2019. Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 25(1):60–64. doi:10.1038/s41591-018-0279-0.
   PubMed Web of Science ®Google Scholar
• Guthrie HC, Owens RW, Bircher MD. 2010. Fractures of the pelvis. J Bone Joint Surg Br. 92-B(11):1481–1488. doi:10.1302/0301-620X.92B11.25911.
   Google Scholar
• Hak DJ, Smith WR, Suzuki T. 2009. Management of hemorrhage in life-threatening pelvic fracture. J Am Acad Orthop Surg. 17(7):447–457. doi:10.5435/00124635-200907000-00005.
   PubMed Web of Science ®Google Scholar
• Hastie T, Tibshirani R, Friedman J. 2009. The elements of statistical learning. New York: Springer. doi:10.1007/978-0-387-84858-7
   Google Scholar
• Hauschild O, Strohm PC, Culemann U, et al. 2008. Mortality in patients with pelvic fractures: results from the German pelvic injury register. J Trauma Inj Infect Crit Care. 64(2):449–455. doi:10.1097/TA.0b013e31815982b1.
   PubMedGoogle Scholar
• Held E, Cape J, Tintle N. 2016. Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data. BMC Proc. 10(S7):34. doi:10.1186/s12919-016-0020-2.
   Google Scholar
• Hou N, Li M, He L, et al. 2020. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 18(1):462. doi:10.1186/s12967-020-02620-5.
   PubMed Web of Science ®Google Scholar
• Hsu Y-T, He Y-T, Ting C-K, Tsou M-Y, Tang G-J, Pu C. 2020. Administrative and claims data help predict patient mortality in intensive care units by logistic regression: a nationwide database study. BioMed Res Int. 2020:9076739. doi:10.1155/2020/9076739.
   PubMed Web of Science ®Google Scholar
• Joachims T. 1998. Text categorization with support vector machines: learning with many relevant features. In: Nédellec C, Rouveirol C, editors. Machine learning: ECML-98. vol 1398. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; p. 137–142. doi:10.1007/BFb0026683
   Google Scholar
• Johnson AEW, Pollard TJ, Shen L, et al. 2016. MIMIC-III, a freely accessible critical care database. Sci Data. 3(1):160035. doi:10.1038/sdata.2016.35.
   PubMed Web of Science ®Google Scholar
• Kuhn M, Johnson K. 2013. Applied predictive modeling. New York: Springer. doi:10.1007/978-1-4614-6849-3
   Google Scholar
• Le Gall JR. 1993. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA J Am Med Assoc. 270(24):2957–2963. doi:10.1001/jama.270.24.2957.
   PubMed Web of Science ®Google Scholar
• Lin K, Hu Y, Kong G. 2019. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model. Int J Med Inf. 125:55–61. doi:10.1016/j.ijmedinf.2019.02.002.
   PubMed Web of Science ®Google Scholar
• Long WJ, Griffith JL, Selker HP, D’Agostino RB. 1993. A comparison of logistic regression to decision-tree induction in a medical domain. Comput Biomed Res Int J. 26(1):74–97. doi:10.1006/cbmr.1993.1005.
   PubMedGoogle Scholar
• Mao Y, Chen Y, Hackmann G, et al. 2011. Medical data mining for early deterioration warning in general hospital wards. In: 2011 IEEE 11th International Conference on Data Mining Workshops. IEEE; p. 1042–1049. doi:10.1109/ICDMW.2011.117
   Google Scholar
• Mardanpour K, Mardanpour N, Elham Zarei MR. 2018. Incidence of pelvic trauma and relative risk factors for mortality: a population based study in Iran. Clin Pract. Published Online. doi:10.4172/clinical-practice.1000401.
   Google Scholar
• Papakostidis C, Giannoudis PV. 2009. Pelvic ring injuries with haemodynamic instability: efficacy of pelvic packing, a systematic review. Injury. 40:S53–S61. doi:10.1016/j.injury.2009.10.037.
   PubMed Web of Science ®Google Scholar
• Pirracchio R, Petersen ML, Carone M, Rigon MR, Chevret S, van der Laan MJ. 2015. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med. 3(1):42–52. doi:10.1016/S2213-2600(14)70239-5.
   PubMed Web of Science ®Google Scholar
• Quartararo M, Glasziou P, Kerr CB. 1995. Classification trees for decision making in long-term care. J Gerontol A Biol Sci Med Sci. 50(6):M298–M302. doi:10.1093/gerona/50a.6.m298.
   PubMed Web of Science ®Google Scholar
• Ravizza S, Huschto T, Adamov A, et al. 2019. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nat Med. 25(1):57–59. doi:10.1038/s41591-018-0239-8.
   PubMed Web of Science ®Google Scholar
• Singal AG, Mukherjee A, Elmunzer JB, et al. 2013. Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma. Am J Gastroenterol. 108(11):1723–1730. doi:10.1038/ajg.2013.332.
   PubMed Web of Science ®Google Scholar
• Stylianou N, Akbarov A, Kontopantelis E, Buchan I, Dunn KW. 2015. Mortality risk prediction in burn injury: comparison of logistic regression with machine learning approaches. Burns. 41(5):925–934. doi:10.1016/j.burns.2015.03.016.
   PubMed Web of Science ®Google Scholar
• Testerman GM, West MR, Hensley S. 2013. Full-time orthopedic traumatologists enhance value and increase pelvic fracture caseloads at a rural level I trauma center. Am Surg. 79(5):549–550.
   PubMed Web of Science ®Google Scholar
• Tsien CL, Fraser HS, Long WJ, Kennedy RL. 1998. Using classification tree and logistic regression methods to diagnose myocardial infarction. Stud Health Technol Inform. 52(Pt 1):493–497.
   PubMedGoogle Scholar
• Vapnik VN. 1998. Statistical learning theory. Wiley.
   Google Scholar
• Venables WN, Ripley BD. 2002. Modern applied statistics with S. New York: Springer. doi:10.1007/978-0-387-21706-2
   Google Scholar
• Wang H, Robinson RD, Moore B, et al. 2016. Predictors of early versus late mortality in pelvic trauma patients. Scand J Trauma Resusc Emerg Med. 24(1):1–8. doi:10.1186/s13049-016-0220-9.
   PubMedGoogle Scholar
• Yang L, Wu H, Jin X, et al. 2020. Study of cardiovascular disease prediction model based on random forest in eastern China. Sci Rep. 10(1):1–8. doi:10.1038/s41598-020-62133-5.
   PubMed Web of Science ®Google Scholar