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运用特殊数据科学分析探究儿童肥胖与10年间心血管变化的关联。

The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis.

作者信息

Cordeiro João Rala, Mosca Sara, Correia-Costa Ana, Ferreira Cátia, Pimenta Joana, Correia-Costa Liane, Barros Henrique, Postolache Octavian

机构信息

Instituto de Telecomunicações, IT-IUL, Iscte-Instituto Universitário de Lisboa, 1649-026 Lisbon, Portugal.

Pediatric Nephrology Unit, Centro Materno-Infantil do Norte, Centro Hospitalar Universitário de Santo António, 4099-001 Porto, Portugal.

出版信息

Children (Basel). 2023 Oct 5;10(10):1655. doi: 10.3390/children10101655.

DOI:10.3390/children10101655
PMID:37892318
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10605863/
Abstract

The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains.

摘要

超重和肥胖患病率的不断上升是一个全球性问题，会带来一些众所周知的后果，这些后果可能在儿童时期就开始显现。本研究基于一项先前建立的队列研究（葡萄牙波尔图的二十一世纪队列研究）中自出生起就被跟踪的儿童数据，利用包括神经架构搜索（NAS）、可解释人工智能（XAI）和深度学习（DL）在内的最新数据科学技术和方法，旨在探索数据的潜在价值，特别是随访期间进行的心电图（ECG）记录中的数据价值。这些技术的结合使我们能够阐明10岁时已经存在的细微心血管变化，这些变化从心电图分析中很明显，可能是由肥胖引起的。本文讨论了所提出的新方法和技术的新颖组合，以及它们在其他健康领域的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/98625b33d7bd/children-10-01655-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/6e714cec4124/children-10-01655-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/98625b33d7bd/children-10-01655-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/e5e5632a8aed/children-10-01655-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/546abfcf1ada/children-10-01655-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/7e262fecda49/children-10-01655-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/8a822f9045b7/children-10-01655-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/50eb24d77e7a/children-10-01655-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/e58dbdaa2c34/children-10-01655-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/90d4bc93a9cb/children-10-01655-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/6e714cec4124/children-10-01655-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/2ec248a9249f/children-10-01655-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/a804189d0f76/children-10-01655-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/8f329c7d2efc/children-10-01655-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/c0d3b756bf61/children-10-01655-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/140c/10605863/98625b33d7bd/children-10-01655-g013.jpg

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