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

Medical and Biological Engineering and Computing

Volumn 51, Issue 9, 2013, Pages 1059-1067

  Kim, Kyung Ah ^a   Choi, Joon Yul ^b   Yoo, Tae Keun ^c   Kim, Sung Kean ^b,c   Chung, Kilsoo ^d   Kim, Deok Won ^b,c  

^a Chungbuk National University College of Medicine   (South Korea)

^b Yonsei University College of Medicine   (South Korea)

^c Yonsei University   (South Korea)

^d Dongyang Mirae University   (South Korea)

Author keywords

Hemorrhagic shock; Machine learning; Mortality; Rat; Support vector machine

Indexed keywords

HEMORRHAGIC SHOCK; LACTATE CONCENTRATION; LOGISTIC REGRESSIONS; MACHINE LEARNING TECHNIQUES; MEAN ARTERIAL PRESSURE; MORTALITY; SYSTOLIC AND DIASTOLIC BLOOD PRESSURES; VARIABLE SELECTION;

BLOOD PRESSURE; DECISION TREES; FORECASTING; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; RATING; RATS;

SUPPORT VECTOR MACHINES;

LACTIC ACID;

ANIMAL EXPERIMENT; ARTICLE; ARTIFICIAL NEURAL NETWORK; BLOOD VOLUME; BREATHING RATE; DIASTOLIC BLOOD PRESSURE; DISEASE SEVERITY; HEART RATE; HEMORRHAGIC SHOCK; KERNEL METHOD; LACTATE BLOOD LEVEL; MACHINE LEARNING; MALE; MEAN ARTERIAL PRESSURE; MORTALITY; NONHUMAN; PULSE PRESSURE; RANDOM FOREST; RAT; SHOCK; SUPPORT VECTOR MACHINE; SURVIVAL; SYSTOLIC BLOOD PRESSURE; TEMPERATURE; ANIMAL; ARTIFICIAL INTELLIGENCE; BLOOD; BODY TEMPERATURE; HEMODYNAMICS; NONPARAMETRIC TEST; PATHOPHYSIOLOGY; PHYSIOLOGY; RANDOMIZATION; RECEIVER OPERATING CHARACTERISTIC; SPRAGUE DAWLEY RAT; STATISTICAL MODEL;

ANIMALS; ARTIFICIAL INTELLIGENCE; BODY TEMPERATURE; HEMODYNAMICS; LACTIC ACID; LOGISTIC MODELS; MALE; MODELS, STATISTICAL; RANDOM ALLOCATION; RATS; RATS, SPRAGUE-DAWLEY; RESPIRATORY RATE; ROC CURVE; SHOCK, HEMORRHAGIC; STATISTICS, NONPARAMETRIC; SUPPORT VECTOR MACHINES;

EID: 84882255796     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11517-013-1091-0     Document Type: Article

References (33)

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
2. American College of Surgeons (2008) ATLS: advanced trauma life support for doctors (student course manual), 8th edn. Am Coll of Surg, Chicago
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
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
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
7. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273-297
8. Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1-2):155-176
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
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
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
13. Huang J, Ling CX (2005) Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299-310
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
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
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
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
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
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
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
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
24. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinforma 23(19):2507-2517
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
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
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
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
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
30. World Health Organization (2010) World health statistics 2010. Switzerland, World Health Organization Press, Geneva, pp 62-70
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
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