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Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China

BACKGROUND: In recent years, the increasing incidence and prevalence of stroke has brought a heavy economic burden on families and society in China. The Ministry of Health of the Peoples’ Republic of China initiated the national stroke screening and intervention program in 2011 for stroke prevention...

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Autores principales: Li, Xuemeng, Pang, Jianfei, Li, Mei, Zhao, Dongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454672/
https://www.ncbi.nlm.nih.gov/pubmed/30961589
http://dx.doi.org/10.1186/s12911-019-0753-8
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author Li, Xuemeng
Pang, Jianfei
Li, Mei
Zhao, Dongsheng
author_facet Li, Xuemeng
Pang, Jianfei
Li, Mei
Zhao, Dongsheng
author_sort Li, Xuemeng
collection PubMed
description BACKGROUND: In recent years, the increasing incidence and prevalence of stroke has brought a heavy economic burden on families and society in China. The Ministry of Health of the Peoples’ Republic of China initiated the national stroke screening and intervention program in 2011 for stroke prevention and control. In the screening, only those who have been classified to “potential high-risk” group in preliminary screening need further examination and physician confirmation to determine the risk level of stroke in rescreening. However, at the beginning of the program, the “potential high-risk” classification method in the preliminary screening are determined by experts based on their experience. The primary aim of this study is to study the causality of stroke and risk factors in middle-aged population using the cohort data, and to explore whether the stroke screening and intervention program should include more precise “potential high-risk” evaluation criteria for this age group in preliminary screening. METHOD: We use the cohort data of screening between 2013 and 2017 in this study. After data cleaning, the cohort consists of 48,007 people aged from 40 to 59 who are free of stroke at baseline. We use Bayesian networks to develop models. RESULT: The results show that the stroke incidence in middle-aged population with certain two risk factors is higher than some of that with three factors, which is in keeping with our previous study results. We can take the ratio of the stroke incidence with combinations of risk factors and incidence without any of the risk factors as a variable threshold. By adjusting the threshold, we can get precise stroke preliminary screening criteria to achieve a balance between economy and efficiency. CONCLUSION: We find that the criteria used in preliminary screening are not reasonable enough. There is a need for national stroke screening and intervention program to further include some more important risk factors or combinations of two risk factors as classification criteria in the preliminary screening. The results of the study can directly guide stroke screening program in China to make the screening more accurate and efficient.
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spelling pubmed-64546722019-04-19 Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China Li, Xuemeng Pang, Jianfei Li, Mei Zhao, Dongsheng BMC Med Inform Decis Mak Research BACKGROUND: In recent years, the increasing incidence and prevalence of stroke has brought a heavy economic burden on families and society in China. The Ministry of Health of the Peoples’ Republic of China initiated the national stroke screening and intervention program in 2011 for stroke prevention and control. In the screening, only those who have been classified to “potential high-risk” group in preliminary screening need further examination and physician confirmation to determine the risk level of stroke in rescreening. However, at the beginning of the program, the “potential high-risk” classification method in the preliminary screening are determined by experts based on their experience. The primary aim of this study is to study the causality of stroke and risk factors in middle-aged population using the cohort data, and to explore whether the stroke screening and intervention program should include more precise “potential high-risk” evaluation criteria for this age group in preliminary screening. METHOD: We use the cohort data of screening between 2013 and 2017 in this study. After data cleaning, the cohort consists of 48,007 people aged from 40 to 59 who are free of stroke at baseline. We use Bayesian networks to develop models. RESULT: The results show that the stroke incidence in middle-aged population with certain two risk factors is higher than some of that with three factors, which is in keeping with our previous study results. We can take the ratio of the stroke incidence with combinations of risk factors and incidence without any of the risk factors as a variable threshold. By adjusting the threshold, we can get precise stroke preliminary screening criteria to achieve a balance between economy and efficiency. CONCLUSION: We find that the criteria used in preliminary screening are not reasonable enough. There is a need for national stroke screening and intervention program to further include some more important risk factors or combinations of two risk factors as classification criteria in the preliminary screening. The results of the study can directly guide stroke screening program in China to make the screening more accurate and efficient. BioMed Central 2019-04-09 /pmc/articles/PMC6454672/ /pubmed/30961589 http://dx.doi.org/10.1186/s12911-019-0753-8 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Li, Xuemeng
Pang, Jianfei
Li, Mei
Zhao, Dongsheng
Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China
title Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China
title_full Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China
title_fullStr Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China
title_full_unstemmed Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China
title_short Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China
title_sort discover high-risk factor combinations using bayesian network from cohort data of national stoke screening in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454672/
https://www.ncbi.nlm.nih.gov/pubmed/30961589
http://dx.doi.org/10.1186/s12911-019-0753-8
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