Clinical Characteristics and Mathematical Analysis of Curative Effect of Hemodialysis in Curing Poisoning Caused by Snakebite

Abstract

In order to explore the clinical characteristics of hemodialysis in curing poisoning from snakebites, a two-classification model of nuclear logistic neural network based on restricted Boltzmann machine is proposed. The model combines kernel logistic regression with artificial neural networks, enabling the model to both learn autonomously and handle linearly inseparable problems. The network first performs feature learning through unsupervised training of restricted Boltzmann machines and obtains the initial values of the parameters to be identified, which reduces the influence of the randomness of the initial parameters. The variable universe learning rate with scaling factor is used to learn the parameters to be identified, and the model convergence speed is improved by dynamic adjustment of the learning rate. Experimental results show the following: Compared with before treatment, patient's activated partial thromboplastin time (APTT) after treatment and the prothrombin time (PT) level decrease, fibrinogen (FIB) levels are elevated, aspartate transferase (AST) and creatine kinase isoenzyme (CK-MB) level decreased, and the differences were statistically significant (p<0.05). It is proved that continuous hemodiafiltration combined with plasma exchange treatment can effectively improve the blood coagulation index and myocardial index of severe snakebite poisoning patients.