Artif Intell Med - Multilevel Bayesian networks for the analysis of hierarchical health care data.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv larg health care dataset normal hierarch structur term level data obtain differ practic hospit region multilevel regress techniqu common use deal multilevel data howev statist analysi interact entiti domain multilevel regress yield littl insight bayesian network prove use analysi interact capabl deal hierarch data paper describ new formal call multilevel bayesian network effect analysi hierarch structur health care data studi perspect multimorbiditymethod multilevel bayesian network formal defin appli analyz clinic data famili practic netherland aim predict interact heart failur diabet mellitus compar result obtain multilevel regressionresult result obtain multilevel bayesian network close resembl obtain multilevel regress diseas area curv predict model improv net reclassif improv signific posit addit model offer consider insight intern structur interact diseasesconclus multilevel bayesian network offer suitabl altern multilevel regress analyz hierarch health care data provid insight interact multipl diseas moreov multilevel bayesian network model can use predict occurr multipl diseas even predictor unknown typic case medicin

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