MIxBN: library for learning Bayesian networks from mixed data

Bubnova, Anna; Deeva, Irina; Kalyuzhnaya, Anna V.

doi:10.1016/j.procs.2021.10.051

Cited by 4 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Learning DAG and parameters of joint probability distribution are similar to learning for ordinary BN (algorithm in the item 2.2). All networks learn using the BAMT python package [ 31 , 32 ]. We validate DBN using only test samples.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

View full text Add to dashboard Cite

The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

show abstract

Section: Methodsmentioning

confidence: 99%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

View full text Add to dashboard Cite

show abstract

A Multimodal Approach to Synthetic Personal Data Generation with Mixed Modelling: Bayesian Networks, GAN’s and Classification Models

Deeva

Mossyayev

Kalyuzhnaya

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

NOTEARS-M: Causal Bayesian Network Structure Learning of Mixed Type Data and Its Application in Identifying Disease Risk Factors

Zhao,

Jia

2024

Preprint

View full text Add to dashboard Cite

Background Identifying and understanding disease risk factors is crucial in epidemiology, particularly for chronic and noncommunicable diseases that often have complex interrelationships. Traditional statistical methods struggle to capture these complexities, necessitating more sophisticated analytical frameworks. Bayesian networks and directed acyclic graphs (DAGs) provide powerful tools for exploring the complex relationships between variables. However, existing DAG structure learning algorithms still have limitations in handling mixed-type data (including continuous and discrete variables), which restricts their practical utility. Therefore, developing DAG structure learning methods that can effectively handle mixed data is highly important for obtaining an in-depth understanding of disease risk factors and pathogenic mechanisms. Methods This study proposes an extension of the NOTEARS algorithm, termed NOTEARS-M, which is designed for Bayesian network structure learning with mixed-type data. The algorithm integrates continuous and categorical variables through a tailored loss function, enhancing its applicability to real-world epidemiological datasets. Results Extensive simulations were conducted across eight distinct scenarios, specifically, variations in the number of nodes, changes in the proportion of categorical nodes, different sample sizes, levels of categorical nodes, variations in edge sparsity, adjustments to the weight scale, different graph types, and diverse noise distributions. These scenarios demonstrate that NOTEARS-M consistently outperforms existing methods such as MMHC, mDAG, and DAGBagM across key metrics, including precision, recall, F1 score, and structural Hamming distance (SHD). Furthermore, the robustness of NOTEARS-M is validated through its application to the National Health and Nutrition Examination Survey (NHANES) dataset, revealing critical causal relationships among risk factors for CHD and diabetes. Conclusions NOTEARS-M provides a powerful and scalable tool for uncovering causal relationships in complex disease networks, with significant implications for risk factor identification and public health research.

show abstract

MIxBN: library for learning Bayesian networks from mixed data

Cited by 4 publications

References 11 publications

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

A Multimodal Approach to Synthetic Personal Data Generation with Mixed Modelling: Bayesian Networks, GAN’s and Classification Models

NOTEARS-M: Causal Bayesian Network Structure Learning of Mixed Type Data and Its Application in Identifying Disease Risk Factors

Contact Info

Product

Resources

About