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PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China

Epidemiological knowledge of pediatric diseases may improve professionals’ understanding of the pathophysiology of and risk factors for diseases and is also crucial for decision making related to workforce and resource planning in pediatric departments. In this study, a pediatric disease epidemiolog...

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Autores principales: Li, Haomin, Yu, Gang, Dong, Cong, Jia, Zheng, An, Jiye, Duan, Huilong, Shu, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883068/
https://www.ncbi.nlm.nih.gov/pubmed/31780760
http://dx.doi.org/10.1038/s41598-019-54439-w
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author Li, Haomin
Yu, Gang
Dong, Cong
Jia, Zheng
An, Jiye
Duan, Huilong
Shu, Qiang
author_facet Li, Haomin
Yu, Gang
Dong, Cong
Jia, Zheng
An, Jiye
Duan, Huilong
Shu, Qiang
author_sort Li, Haomin
collection PubMed
description Epidemiological knowledge of pediatric diseases may improve professionals’ understanding of the pathophysiology of and risk factors for diseases and is also crucial for decision making related to workforce and resource planning in pediatric departments. In this study, a pediatric disease epidemiology knowledgebase called PedMap (http://pedmap.nbscn.org) was constructed from the clinical data from 5 447 202 outpatient visits of 2 189 868 unique patients at a children’s hospital (Hangzhou, China) from 2013 to 2016. The top 100 most-reported pediatric diseases were identified and visualized. These common pediatric diseases were clustered into 4 age groups and 4 seasons. The prevalence, age distribution and co-occurrence diseases for each disease were also visualized. Furthermore, an online prediction tool based on Gaussian regression models was developed to predict pediatric disease incidence based on weather information. PedMap is the first comprehensive epidemiological resource to show the full view of age-related, seasonal, climate-related variations in and co-occurrence patterns of pediatric diseases.
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spelling pubmed-68830682019-12-31 PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China Li, Haomin Yu, Gang Dong, Cong Jia, Zheng An, Jiye Duan, Huilong Shu, Qiang Sci Rep Article Epidemiological knowledge of pediatric diseases may improve professionals’ understanding of the pathophysiology of and risk factors for diseases and is also crucial for decision making related to workforce and resource planning in pediatric departments. In this study, a pediatric disease epidemiology knowledgebase called PedMap (http://pedmap.nbscn.org) was constructed from the clinical data from 5 447 202 outpatient visits of 2 189 868 unique patients at a children’s hospital (Hangzhou, China) from 2013 to 2016. The top 100 most-reported pediatric diseases were identified and visualized. These common pediatric diseases were clustered into 4 age groups and 4 seasons. The prevalence, age distribution and co-occurrence diseases for each disease were also visualized. Furthermore, an online prediction tool based on Gaussian regression models was developed to predict pediatric disease incidence based on weather information. PedMap is the first comprehensive epidemiological resource to show the full view of age-related, seasonal, climate-related variations in and co-occurrence patterns of pediatric diseases. Nature Publishing Group UK 2019-11-28 /pmc/articles/PMC6883068/ /pubmed/31780760 http://dx.doi.org/10.1038/s41598-019-54439-w Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Li, Haomin
Yu, Gang
Dong, Cong
Jia, Zheng
An, Jiye
Duan, Huilong
Shu, Qiang
PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China
title PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China
title_full PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China
title_fullStr PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China
title_full_unstemmed PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China
title_short PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China
title_sort pedmap: a pediatric diseases map generated from clinical big data from hangzhou, china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883068/
https://www.ncbi.nlm.nih.gov/pubmed/31780760
http://dx.doi.org/10.1038/s41598-019-54439-w
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