Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian and likelihood methods for fitting multilevel models with complex level-1 variation
Computational Statistics & Data Analysis
Extension of the HEPAR II Model to Multiple-Disorder Diagnosis
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Learning Bayesian Networks
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
Learning patient-specific predictive models from clinical data
Journal of Biomedical Informatics
Modelling patterns of evidence in Bayesian networks: a case-study in classical swine fever
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Incorporating expert knowledge when learning Bayesian network structure: A medical case study
Artificial Intelligence in Medicine
Data analysis with bayesian networks: a bootstrap approach
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Exact inference in networks with discrete children of continuous parents
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks
Artificial Intelligence in Medicine
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
Paper: Specification of models in large expert systems based on causal probabilistic networks
Artificial Intelligence in Medicine
Guest editorial: Probabilistic problem solving in biomedicine
Artificial Intelligence in Medicine
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Objective: Large health care datasets normally have a hierarchical structure, in terms of levels, as the data have been obtained from different practices, hospitals, or regions. Multilevel regression is the technique commonly used to deal with such multilevel data. However, for the statistical analysis of interactions between entities from a domain, multilevel regression yields little to no insight. While Bayesian networks have proved to be useful for analysis of interactions, they do not have the capability to deal with hierarchical data. In this paper, we describe a new formalism, which we call multilevel Bayesian networks; its effectiveness for the analysis of hierarchically structured health care data is studied from the perspective of multimorbidity. Methods: Multilevel Bayesian networks are formally defined and applied to analyze clinical data from family practices in The Netherlands with the aim to predict interactions between heart failure and diabetes mellitus. We compare the results obtained with multilevel regression. Results: The results obtained by multilevel Bayesian networks closely resembled those obtained by multilevel regression. For both diseases, the area under the curve of the prediction model improved, and the net reclassification improvements were significantly positive. In addition, the models offered considerable more insight, through its internal structure, into the interactions between the diseases. Conclusions: Multilevel Bayesian networks offer a suitable alternative to multilevel regression when analyzing hierarchical health care data. They provide more insight into the interactions between multiple diseases. Moreover, a multilevel Bayesian network model can be used for the prediction of the occurrence of multiple diseases, even when some of the predictors are unknown, which is typically the case in medicine.