Med Biol Eng Comput - Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques.

Tópicos

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Resumo

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.

Resumo Limpo

studi sought determin mortal predict model use triag set acut hemorrhag trauma achiev aim various machin learn techniqu appli use rat model acut hemorrhag thirtysix anesthet rat random three group accord volum control blood loss measur includ heart rate hr systol diastol blood pressur sbp dbp mean arteri pressur puls pressur respiratori rate temperatur blood lactat concentr lc peripher perfus pp shock index si si hrsbp new hemorrhageinduc sever index ni ni lcpp ni suggest one good candid mortal predict variabl previous studi construct mortal predict model logist regress lr artifici neural network ann random forest rf support vector machin svm variabl select svm model show better sensit area curv lr ann rf model mortal predict import variabl select svm ni lc svm model may help first respond need make accur triag decis rapid treat hemorrhag patient case trauma

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