Towards Automatic Generation of Ontology-Based Antipattern Bayesian Network Models

Minimum training sample size requirements for achieving high prediction accuracy with the BN model: A case study regarding seismic liquefaction

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

ObjectiveThis study aims to identify relevant risk factors, assess the interactions between variables, and establish a predictive model for ischemic stroke (IS) in patients with cardiac myxoma (CM) using the Bayesian network (BN) approach.MethodsData of patients with CM were collected from three tertiary comprehensive hospitals in Beijing from January 2002 to January 2022. Age, sex, medical history, and information related to CM were extracted from the electronic medical record system. The BN model was constructed using the tabu search algorithm, and the conditional probability of each node was calculated using the maximum likelihood estimation method. The probability of each node of the network and the interrelationship between IS and its related factors were qualitatively and quantitatively analyzed. A receiver operating characteristic (ROC) curve was also plotted. Sensitivity, specificity, and area under the curve (AUC) values were calculated and compared between the BN and logistic regression models to evaluate the efficiency of the predictive model.ResultsA total of 416 patients with CM were enrolled in this study, including 61 with and 355 without IS. The BN model found that cardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility were directly associated with the occurrence of IS in patients with CM. The BN model for predicting CM-IS achieved higher scores on AUC {0.706 [95% confidence interval (CI), 0.639–0.773]} vs. [0.697 (95% CI, 0.629–0.766)] and sensitivity (99.44% vs. 98.87%), but lower scores on accuracies (85.82% vs. 86.06%) and specificity (6.56% vs. 11.48%) than the logistic regression model.ConclusionCardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility are candidate predictors of IS in patients with CM. The BN model was superior or at least non-inferior to the traditional logistic regression model, and hence is potentially useful for early IS detection, diagnosis, and prevention in clinical practice.

Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.

Climate and land-use changes are posing increasing threats to freshwater-related ecosystem services, acting both on the supply and demand sides. These changes disrupt critical processes such as nutrient cycling, sediment transport, and water flow regulation, leading to declining water quality and reduced ecosystem resilience. There is an urgent need for a deeper understanding of the dynamics of these threats, which can help enhance water management, environmental protection, and human well-being. To effectively tackle these risks, it is essential to quantitatively combine physical hazards and vulnerabilities by pinpointing hotspots where multiple stressors greatly increase the risk of water quality degradation. In response to this challenge, Bayesian network models offer a promising decision-support tool for evaluating adaptation options for water resource management, as they can integrate both quantitative and qualitative data. Building upon this approach, we developed a Spatial Bayesian Network (SBN) model to predict the probability of potential risks to water quality at the river basin scale in Italy and support the goal of achieving good chemical and ecological status according to the Water Framework Directive. This integrated model incorporates the complex relationships between land use change, climate change indicators (e.g., flood and drought intensity), and their combined impacts on water degradation. First, the baseline model uses historical patterns of climate change metrics from the CMCC DSS dataset and land use indicators from the Corine Land Cover as inputs to generate probabilistic predictions of potential risks to water quality. Then, the relationships between these variables are captured in their conditional probabilities, allowing for quantifying interactions and identifying key stressors, paving the way for scenario analysis. Finally, different future scenarios will be developed to predict the changes in water quality, considering projected climate data and socio-economic conditions. The outcome of this analysis contributes to developing an integrated management strategy that will help water managers make decisions and ultimately improve the resilience of freshwater ecosystems while supporting the implementation of adaptation strategies to address such problems.Keywords: Water Framework Directive, Water quality, Climate change, Land-use/ land-cover change Bayesian Network (BN) model

Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models

Aim. It is essential to understand the extent to which job characteristics impact work-related musculoskeletal disorders (WMSDs), and to calculate the probability that an employee will suffer from a musculoskeletal disorder given their working conditions. The objective of this research is to identify the relationships between WMSDs and working characteristics, by developing a Bayesian network (BN) model to calculate the probability that an employee suffers from a musculoskeletal disorder. Methods. A conceptual model was constructed based on a BN. This was then statistically tested and corrected to establish a BN model. Results. Experiments verified that the BN model achieves a better diagnostic performance than artificial neural network, support vector machine and decision tree approaches, and is robust in diagnosing WMSDs given working characteristics. Conclusion. It was verified that working characteristics, such as working hours and pace, impact the incidence rate of WMSDs, and a BN model was developed to probabilistically diagnose WMSDs.

BackgroundThis study aims to establish a Bayesian network risk prediction model for gastric cancer using data mining methods. It explores both direct and indirect factors influencing the incidence of gastric cancer and reveals the interrelationships among these factors.MethodsData were collected from early cancer screenings conducted at the People’s Hospital of Lincang between 2022 and 2023. Initial variable selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) and Sliding Windows Sequential Forward Selection (SWSFS), and the screened variables and demographic characteristics features were used as variables for constructing the Bayesian network (BN) model. Subsequently, the performance of the models was evaluated, and the optimal model was selected for network mapping and Bayesian inference using the best model.ResultsThe incidence rate of gastric cancer in this region’s high-risk population was determined to be 7.09%. The BN model constructed from the set of variables consisting of Lasso’s selection variables and demographic characteristics had better performance. A total of 12 variables were incorporated into the BN model to form a network structure consisting of 13 nodes and 18 edges. The model shows that age, gender, ethnicity, current address, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, smoking, SGIM gastritis, and family history are important risk factors for gastric cancer development.ConclusionThe Bayesian network model provides an intuitive framework for understanding the direct and indirect factors contributing to the early onset of gastric cancer, elucidating the interrelationships among these factors. Furthermore, the model demonstrates satisfactory predictive performance, which may facilitate the early detection of gastric cancer and enhance the levels of early diagnosis and treatment among high-risk populations.

Stroke-associated pneumonia (SAP) is a major cause of mortality following ischemic stroke (IS). However, existing predictive models for SAP often lack transparency and interpretability, limiting their clinical utility. This study aims to develop an interpretable Bayesian network (BN) model for predicting SAP in IS patients, focusing on enhancing both predictive accuracy and clinical interpretability. This retrospective study included patients diagnosed with IS and admitted to the Second Affiliated Hospital of Nanchang University between January and December 2019. Clinical data collected within 48 h of admission and SAP occurrences within 7 days were analyzed. Dimensionality reduction was performed using Least Absolute Shrinkage and Selection Operator regression, while data imbalances were addressed using synthetic minority oversampling technique. A BN model was trained using a hill-climbing algorithm and compared to logistic regression, decision trees, deep neural networks, and existing risk-scoring systems. Decision curve analysis was used to assess clinical usefulness. Of the 1252 patients, 165 (13.18%) patients had SAP within 7 days of admission. The BN model identified age, risk of pressure injury (PI), National Institutes of Health Stroke Scale (NIHSS) score, and C-reactive protein (CRP) as significant prognostic factors. The BN model achieved an area under the curve of 0.85(95% CI: 0.78-0.92) on the test set, outperforming other models and demonstrating a greater net benefit in clinical decision-making. Age, risk of PI, NIHSS score, and CRP are significant predictors of SAP in IS patients. The interpretable BN model demonstrates superior predictive performance and interpretability, suggesting its potential as an effective and interpretable tool for clinical decision support in SAP risk assessment.

<title>Abstract</title> <bold>Objective</bold> Given the influence of women's unique joint structures and hormonal levels, sports injuries among females have become a focus in sports research. This study aimed to use Logistic regression and Bayesian networks (BNs) models to explore factors associated with sports injuries in Chinese adult females who exercise regularly.<bold>Methods</bold> This was a cross - sectional study. From October to November 2021, data on sports - injury - related factors were collected through online questionnaires from adult females aged 18 and above who exercised regularly in 336 cities across 34 provinces nationwide. Logistic regression and BNs models were used to explore factors associated with sports injuries in Chinese adult females with regular exercise.<bold>Results</bold> A total of 6,912 valid questionnaires were included, with a median age of 34.00 (31.00–39.00) years. Among the participants, 4,265 (61.70%) had experienced sports injuries. Logistic regression indicated that age grouping, body mass index (BMI), the most frequent daily exercise time, learning of specialized movement, insufficient energy to complete daily tasks, fatigue or illness status, sleep quality, and awareness of sports injury risks were all risk factors for sports injuries. BNs revealed that age, sleep quality, the most frequent daily exercise time, and fatigue or illness status were directly correlated with sports injuries. Moreover, exercise venue type and learning of specialized movement were indirectly associated with sports injuries through the mediating variable of the most frequent daily exercise time.<bold>Conclusion</bold> BNs can identify both direct and indirect correlates of sports injuries, and Bayesian risk inference enables risk prediction for sports injuries. BNs serve as a complementary method to logistic regression, providing deeper insights into complex risk factor interactions.

We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed ‘solution’, which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel and simple solution to this problem that works in all cases where the mutually exclusive nodes have no common ancestors. Our solution uses a special type of constraint and auxiliary node together with formulas for assigning their necessary conditional probability table values. The solution enforces mutual exclusivity between events and preserves their prior probabilities while leaving all original BN nodes unchanged.

Recently, the social dimension of sustainability has become a focal point for academia and practitioners. Despite this growing attention, several unresolved challenges still exist within supply chain management (SCM), limiting the attainment of satisfactory levels of sustainability. This study fills this gap by proposing a Bayesian Network (BN) model, an Artificial Intelligence (AI) technique, for assessing the risk probability related to the social dimension of sustainability in supplier contracting. This novel approach utilises BN model incorporating the Human Development Index (HDI) and Global Slavery Index (GSI) to ascertain the likelihood of non-compliance risks in social aspects across apparel supply chains. Findings and testing of the BN model in apparel industry revealed that this model represents a proactive tool for decision-makers and practitioners by facilitating the monitoring and control of social burdens across SC tiers. Empirical analysis successfully identified the risk probability levels of non-compliance integrating company-specific data and external sources. The BN model offers a comprehensive understanding of the supply chain to mitigate a company’s risk and leverage its transparency regarding the social dimension of sustainability. The BN model offers an essential resource for practitioners, decision-makers, and academia to address more effectively the social dimension of sustainability in the SCM.

A Bayesian Network model for risk analysis of deepwater drilling riser fracture failure

Multisource geospatial data contains a lot of information that can be used for environment assessment and management. In this paper, four environmental indicators representing typical human activities in Yellow River Delta, China are extracted from multisource geospatial data. By analysing the causal relationship between these human-related indicators and NDVI, a Bayesian network (BN) model is developed. Part of the raster data pre-processed using GIS is used for training the BN model, and the other data is used for model testing. Sensitivity analysis and performance assessment showed that the BN model was good enough to reveal the impacts of human activities on land vegetation. With the trained BN model, the vegetation change under three different scenarios was also predicted. The results showed that multisource geospatial data could be successfully collated using the GIS-BN framework for vegetation detection.

A method for obtaining the Bayesian network (BN) representation of a sensor's measurement process is developed so that the problems of sensor fusion and management can be approached from a unified point of view. Uncertainty, reliability, and causal information embedded in the sensor data are used to build the BN model of a sensor. The method is applied to model ground-penetrating radar, electromagnetic induction, and infrared sensors for humanitarian demining. Structural and parameter learning algorithms are employed to encode relationships among mine features, sensor measurements, and environmental conditions in the BN model. Inference is used to estimate target features in the presence of heterogeneous soil and varying environmental conditions. A multisensor fusion technique operating on BN models is developed to exploit the complementarity of the sensor measurements. Through the same approach, a BN classifier is obtained to estimate the target typology. The BN models and classifier also compute so-called confidence levels that quantify the uncertainty associated with the feature estimates and the classification decisions. The effectiveness of the approach is demonstrated by implementing these BN tools for the detection and classification of metal and plastic landmines that are characterized by different shape, size, depth, and metal content. Through BN fusion, the accuracy of the feature estimates is improved by up to 64% with respect to single-sensor measurements, and the number of objects that are both detected and classified is increased by up to 62%.