Application of tabu search-based Bayesian networks in exploring related factors of liver cirrhosis complicated with hepatic encephalopathy and disease identification

Abstract

This study aimed to explore the related factors and strengths of hepatic cirrhosis complicated with hepatic encephalopathy (HE) by multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs), and to deduce the probability of HE in patients with cirrhosis under different conditions through BN reasoning. Multivariate logistic regression analysis indicated that electrolyte disorders, infections, poor spirits, hepatorenal syndrome, hepatic diabetes, prothrombin time, and total bilirubin are associated with HE. Inferences by BNs found that infection, electrolyte disorder and hepatorenal syndrome are closely related to HE. Those three variables are also related to each other, indicating that the occurrence of any of those three complications may induce the other two complications. When those three complications occur simultaneously, the probability of HE may reach 0.90 or more. The BN constructed by the tabu search algorithm can analyze not only how the correlative factors affect HE but also their interrelationships. Reasoning using BNs can describe how HE is induced on the basis of the order in which doctors acquire patient information, which is consistent with the sequential process of clinical diagnosis and treatment.

Early Warning Evaluation of Tuberculosis and Meteorological Factors in Shanxi Province Based on Dynamic Bayesian Network

“…March is the peak of the whole year, and spring and early summer have an obvious seasonal increase [2,6,17], which is roughly consistent with previous studies. The results of this study show that meteorological factors have a BN is one of the most effective theoretical models in the uncertain reasoning [19,35]. The DBN early warning model of tuberculosis-meteorological factors established in this study has a signi cantly better classi cation and recognition performance than other models for panel data with spatial and temporal dimensions.…”

“…The traditional epidemiological analysis mainly studies the distribution characteristics of the disease in time, space and population [6,17], while time series analysis can determine whether the occurrence of the disease has periodicity and peak period, and can predict the incidence [18]. DBN has great potential for data mining applications [19], but it has few in medicine, which are mainly in gene regulatory Networks. In 2010, Lingling Ge studied the construction method of gene regulatory Network based on dynamic Bayesian model.…”

“…The time series analysis commonly used in this data, which mainly constructed by time series analysis method at present. The DBN model is one of the effective models, it is accepted by more and more people because of its ability in generality and data mining [19]. 3.…”

Early Warning Evaluation of Tuberculosis and Meteorological Factors in Shanxi Province based on Dynamic Bayesian Network

“…The traditional epidemiological analysis mainly studies the distribution characteristics of the disease in time, space and population 6, 17 , while time series analysis can determine whether the occurrence of the disease has periodicity and peak period, and can predict the incidence 18 . DBN has great potential for data mining applications 19 , but it has few in medicine, which are mainly in gene regulatory Networks. In 2010, Lingling Ge studied the construction method of gene regulatory Network based on dynamic Bayesian model.…”

“…The time series analysis commonly used in this data, which mainly constructed by time series analysis method at present. The DBN model is one of the effective models, it is accepted by more and more people because of its ability in generality and data mining 19 .…”