Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques
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.
mortality rat hemorrhagic shock machine learning support vector machine