Due to the high heterogeneity of hospital patients and the spatiotemporal fluctuations of external intervention measures, the transmission of nosocomial infections presents non-linear and stochastic characteristics, which increases the difficulty of risk prediction. Therefore, a risk prediction algorithm for nosocomial infection transmission based on Bayesian networks and Monte Carlo simulations is proposed. The factors influencing the risk of nosocomial infection transmission are screened from four dimensions: patients,medical operations, environment and management. The categorical variables are converted into continuous variables to unify the variable dimensions, which is convenient for subsequent calculations. The objective weighting method is introduced to obtain the weight of a single factor, and the subjective weights are integrated to obtain the comprehensive weight of the influencing factors, so as to rank the importance of the influencing factors. An infection transmission risk prediction model is constructed by using Bayesian networks to calculate the joint probability distribution of high-weight influence factors and maximum likelihood estimation. The Monte Carlo simulation algorithm is utilized to randomly sample the uncertain variables in the model, generating simulation scenarios. Based on the statistical simulation results, the probability of infection transmission risk is calculated to determine the risk situation. The experimental results show that when the proposed method is applied to predict the risk of nosocomial infection transmission, the specificity of the prediction results is higher than 90% , false alarms are reduced, and the prediction accuracy is higher.

bayesian network, Monte Carlo simulation, risk of nosocomial infection transmission, prediction model, Influencing factors