Bayesian network-based missing mechanism identification (BN-MMI) method in medical research

Abstract Background Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some p...

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Autores principales:	Tingyan Yue, Tao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/aa28a113b0b844aca12f6c6fa2088821
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spelling	oai:doaj.org-article:aa28a113b0b844aca12f6c6fa20888212021-11-14T12:29:13ZBayesian network-based missing mechanism identification (BN-MMI) method in medical research10.1186/s12911-021-01677-61472-6947https://doaj.org/article/aa28a113b0b844aca12f6c6fa20888212021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01677-6https://doaj.org/toc/1472-6947Abstract Background Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research. Methods The procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research. Results The simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data. Conclusions It was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.Tingyan YueTao ZhangBMCarticleComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Tingyan Yue Tao Zhang Bayesian network-based missing mechanism identification (BN-MMI) method in medical research
description	Abstract Background Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research. Methods The procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research. Results The simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data. Conclusions It was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.
format	article
author	Tingyan Yue Tao Zhang
author_facet	Tingyan Yue Tao Zhang
author_sort	Tingyan Yue
title	Bayesian network-based missing mechanism identification (BN-MMI) method in medical research
title_short	Bayesian network-based missing mechanism identification (BN-MMI) method in medical research
title_full	Bayesian network-based missing mechanism identification (BN-MMI) method in medical research
title_fullStr	Bayesian network-based missing mechanism identification (BN-MMI) method in medical research
title_full_unstemmed	Bayesian network-based missing mechanism identification (BN-MMI) method in medical research
title_sort	bayesian network-based missing mechanism identification (bn-mmi) method in medical research
publisher	BMC
publishDate	2021
url	https://doaj.org/article/aa28a113b0b844aca12f6c6fa2088821
work_keys_str_mv	AT tingyanyue bayesiannetworkbasedmissingmechanismidentificationbnmmimethodinmedicalresearch AT taozhang bayesiannetworkbasedmissingmechanismidentificationbnmmimethodinmedicalresearch
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Bayesian network-based missing mechanism identification (BN-MMI) method in medical research

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