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Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques

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Abstract

This study sought to determine a mortality prediction model that could be used for triage in the setting of acute hemorrhage from trauma. To achieve this aim, various machine learning techniques were applied using the rat model in acute hemorrhage. Thirty-six anesthetized rats were randomized into three groups according to the volume of controlled blood loss. Measurements included heart rate (HR), systolic and diastolic blood pressures (SBP and DBP), mean arterial pressure, pulse pressure, respiratory rate, temperature, blood lactate concentration (LC), peripheral perfusion (PP), shock index (SI, SI = HR/SBP), and a new hemorrhage-induced severity index (NI, NI = LC/PP). NI was suggested as one of the good candidates for mortality prediction variable in our previous study. We constructed mortality prediction models with logistic regression (LR), artificial neural networks (ANN), random forest (RF), and support vector machines (SVM) with variable selection. The SVM model showed better sensitivity (1.000) and area under curve (0.972) than the LR, ANN, and RF models for mortality prediction. The important variables selected by the SVM were NI and LC. The SVM model may be very helpful to first responders who need to make accurate triage decisions and rapidly treat hemorrhagic patients in cases of trauma.

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References

  1. Agranoff D, Fernandez-Reyes D, Papadopoulos MC, Rojas SA, Herbster M, Loosemore A, Tarelli E, Sheldon J, Schwenk A, Pollok R, Rayner CF, Krishna S (2006) Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Lancet 368(9540):1012–1021

    Article  PubMed  CAS  Google Scholar 

  2. American College of Surgeons (2008) ATLS: advanced trauma life support for doctors (student course manual), 8th edn. Am Coll of Surg, Chicago

    Google Scholar 

  3. Benedek DM, Fullerton C, Ursano RJ (2007) First responders: mental health consequences of natural and human-made disasters for public health and public safety workers. Annu Rev Public Health 28:55–68

    Article  PubMed  Google Scholar 

  4. Birkhahn RH, Gaeta TJ, Terry D, Bove JJ, Tloczkowski J (2005) Shock index in diagnosing early acute hypovolemia. Am J Emerg Med 23(3):323–326

    Article  PubMed  Google Scholar 

  5. Choi JY, Kim SK, Lee WH, Yoo TK, Kim DW (2012) A survival prediction model of rats in hemorrhagic shock using the random forest classifier. Proceedings of the 34th International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, IEEE Service Center, Piscataway, NJ, pp 5570–5573. doi: 10.1109/IEMBS.2012.6347256

  6. Choi JY, Lee WH, Yoo TK, Park I, Kim DW (2012) A new severity predicting index for hemorrhagic shock using lactate concentration and peripheral perfusion in a rat model. Shock 38(6):635–641

    Article  PubMed  CAS  Google Scholar 

  7. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  8. Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1–2):155–176

    Article  Google Scholar 

  9. Davis JA (2008) Mouse and rat anesthesia and analgesia. Curr Protoc Neurosci (Appendix 4: Appendix 4B). doi:10.1002/0471142301.sna04bs42

  10. Dutton RP (2007) Current concepts in hemorrhagic shock. Anesthesiol Clin 25(1):23–34

    Article  PubMed  CAS  Google Scholar 

  11. 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

    Article  PubMed  Google Scholar 

  12. Hsieh CH, Lu RH, Lee NH, Chiu WT, Hsu MH, Li YC (2011) Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks. Surgery 149(1):87–93

    Article  PubMed  Google Scholar 

  13. Huang J, Ling CX (2005) Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299–310

    Article  CAS  Google Scholar 

  14. Jang KH, Yoo TK, Choi JY, Nam KC, Choi JL, Kwon MK, Kim DW (2011) Comparison of survival predictions for rats with hemorrhagic shocks using an artificial neural network and support vector machine. Proceedings of the 33rd International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, IEEE Service Center, Piscataway, NJ, pp 91–94 doi:10.1109/IEMBS.2011.6089904

  15. Kauvar DS, Lefering R, Wade CE (2006) Impact of hemorrhage on trauma outcome: an overview of epidemiology, clinical presentations, and therapeutic considerations. J Trauma 60(Suppl. 6):S3–S11

    Article  PubMed  Google Scholar 

  16. Lerner EB, Schwartz RB, Coule PL, Weinstein ES, Cone DC, Hunt RC, Sasser SM, Liu JM, Nudell NG, Wedmore LS, Hammond J, Bulger EM, Salomone JP, Sanddal TL, Markenson D, O′Connor RE (2008) Mass casualty triage: an evaluation of the data and development of a proposed national guideline. Disaster Med Public Health Prep 2(Suppl 1):S25–S34

    Article  PubMed  Google Scholar 

  17. Letson HL, Dobson GP (2011) Unexpected 100% survival following 60% blood loss using small-volume 7.5% NaCl with adenocaine and Mg (2+) in the rat model of extreme hemorrhagic shock. Shock 36(6):586–594

    Article  PubMed  CAS  Google Scholar 

  18. Lomas-Niera JL, Perl M, Chung CS, Ayala A (2005) Shock and hemorrhage: an overview of animal models. Shock 24(Suppl 1):33–39

    Article  PubMed  Google Scholar 

  19. Macias CA, Kameneva MV, Tenhunen JJ, Puyana JC, Fink MP (2004) Survival in a rat model of lethal hemorrhagic shock is prolonged following resuscitation with a small volume of a solution containing a drag-reducing polymer derived from aloe vera. Shock 22(2):151–156

    Article  PubMed  CAS  Google Scholar 

  20. Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. Proceedings of the 2002 International Joint Conference on Neural Networks, pp 1702–1707. doi:10.1109/IJCNN.2002.1007774

  21. Nakasone Y, Ikeda O, Yamashita Y, Kudoh K, Shigematsu Y, Harada K (2007) Shock index correlates with extravasation on angiographs of gastrointestinal hemorrhage: a logistics regression analysis. Cardiovasc Intervent Radiol 30(5):861–865

    Article  PubMed  Google Scholar 

  22. Not LG, Marchase RB, Fulop N, Brocks CA, Chatham JC (2007) Glucosamine administration improves survival rate after severe hemorrhagic shock combined with trauma in rats. Shock 28(3):345–352

    Article  PubMed  CAS  Google Scholar 

  23. Roberts DA, Holcomb JB, Parker BE Jr, Sondeen JL, Pusateri AE, Brady WJ Jr, Sweenor DE, Young JS (2002) The use of polynomial neural networks for mortality prediction in uncontrolled venous and arterial hemorrhage. J Trauma 52(1):130–135

    Article  PubMed  Google Scholar 

  24. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinforma 23(19):2507–2517

    Article  CAS  Google Scholar 

  25. Schull MJ, Morrison LJ, Vermeulen M, Redelmeier DA (2003) Emergency department overcrowding and ambulance transport delays for patients with chest pain. CMAJ 168(3):277–283

    PubMed  Google Scholar 

  26. Statnikov A, Wang L, Aliferis CF (2008) A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 9:319. doi:10.1186/1471-2105-9-319

    Article  PubMed  Google Scholar 

  27. Takasu A, Sakamoto T, Okada Y (2006) Effect of induction rate for mild hypothermia on survival time during uncontrolled hemorrhagic shock in rats. J Trauma 61(6):1330–1335

    Article  PubMed  Google Scholar 

  28. Waldron L, Pintilie M, Tsao MS, Shepherd FA, Huttenhower C, Jurisica I (2011) Optimized application of penalized regression methods to diverse genomic data. Bioinformatics 27(24):3399–3406

    Article  PubMed  CAS  Google Scholar 

  29. Wilson M, Davis DP, Coimbra R (2003) Diagnosis and monitoring of hemorrhagic shock during the initial resuscitation of multiple trauma patients: a review. J Emerg Med 24(4):413–422

    Article  PubMed  Google Scholar 

  30. World Health Organization (2010) World health statistics 2010. Switzerland, World Health Organization Press, Geneva, pp 62–70

    Google Scholar 

  31. Yang S, Zou LY, Bounelis P, Chaudry I, Chatham JC, Marchase RB (2006) Glucosamine administration during resuscitation improves organ function after trauma hemorrhage. Shock 25(6):600–607

    Article  PubMed  CAS  Google Scholar 

  32. Zhao ZD, Lou YY, Ni JH, Zhang J (2012) RBF-SVM and its application on reliability evaluation of electric power system communication network. In: Machine Learning Cybernetics (ICMLC), 2012 Annual International Conference of the IEEE, pp.1188–1193. doi: 10.1109/ICMLC.2009.5212365

  33. Zou H, Hastie T (2008) Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol 67(2):301–320

    Article  Google Scholar 

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Acknowledgments

This study was supported by a faculty research Grant from Yonsei University College of Medicine for 2011 (6-2011-0087).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Chungbuk National University, College of Medicine, Cheongju, Korea

    Kyung-Ah Kim

  2. Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Korea

    Joon Yul Choi, Sung Kean Kim & Deok Won Kim

  3. Department of Medicine, Yonsei University, Seoul, Korea

    Tae Keun Yoo

  4. Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea

    Sung Kean Kim & Deok Won Kim

  5. Department of Electrical System, DongYang Mirae University, Seoul, Korea

    KilSoo Chung

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  1. Kyung-Ah Kim
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  2. Joon Yul Choi
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  4. Sung Kean Kim
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Correspondence to Deok Won Kim.

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Kim, KA., Choi, J.Y., Yoo, T.K. et al. Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Med Biol Eng Comput 51, 1059–1067 (2013). https://doi.org/10.1007/s11517-013-1091-0

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  • DOI: https://doi.org/10.1007/s11517-013-1091-0

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