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Screening for chronic conditions with reproductive factors using a machine learning based approach
A large proportion of cases with chronic conditions including diabetes or pre-diabetes, hypertension and dyslipidemia remain undiagnosed. To include reproductive factors (RF) might be able to improve current screening guidelines by providing extra effectiveness. The objective is to study the relatio...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028713/ https://www.ncbi.nlm.nih.gov/pubmed/32071372 http://dx.doi.org/10.1038/s41598-020-59825-3 |
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author | Tian, Siyu Dong, Weinan Chan, Ka Lung Leng, Xinyi Bedford, Laura Elizabeth Liu, Jia |
author_facet | Tian, Siyu Dong, Weinan Chan, Ka Lung Leng, Xinyi Bedford, Laura Elizabeth Liu, Jia |
author_sort | Tian, Siyu |
collection | PubMed |
description | A large proportion of cases with chronic conditions including diabetes or pre-diabetes, hypertension and dyslipidemia remain undiagnosed. To include reproductive factors (RF) might be able to improve current screening guidelines by providing extra effectiveness. The objective is to study the relationships between RFs and chronic conditions’ biomarkers. A cross-sectional study was conducted. Demographics, RFs and metabolic biomarkers were collected. The relationship of the metabolic biomarkers were shown by correlation analysis. Principal component analysis (PCA) and autoencoder were compared by cross-validation. The better one was adopted to extract a single marker, the general chronic condition (GCC), to represent the body’s chronic conditions. Multivariate linear regression was performed to explore the relationship between GCC and RFs. In total, 1,656 postmenopausal females were included. A multi-layer autoencoder outperformed PCA in the dimensionality reduction performance. The extracted variable by autoencoder, GCC, was verified to be representative of three chronic conditions (AUC for patoglycemia, hypertension and dyslipidemia were 0.844, 0.824 and 0.805 respectively). Linear regression showed that earlier age at menarche (OR = 0.9976) and shorter reproductive life span (OR = 0.9895) were associated with higher GCC. Autoencoder performed well in the dimensionality reduction of clinical metabolic biomarkers. Due to high accessibility and effectiveness, RFs have potential to be included in screening tools for general chronic conditions and could enhance current screening guidelines. |
format | Online Article Text |
id | pubmed-7028713 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70287132020-02-26 Screening for chronic conditions with reproductive factors using a machine learning based approach Tian, Siyu Dong, Weinan Chan, Ka Lung Leng, Xinyi Bedford, Laura Elizabeth Liu, Jia Sci Rep Article A large proportion of cases with chronic conditions including diabetes or pre-diabetes, hypertension and dyslipidemia remain undiagnosed. To include reproductive factors (RF) might be able to improve current screening guidelines by providing extra effectiveness. The objective is to study the relationships between RFs and chronic conditions’ biomarkers. A cross-sectional study was conducted. Demographics, RFs and metabolic biomarkers were collected. The relationship of the metabolic biomarkers were shown by correlation analysis. Principal component analysis (PCA) and autoencoder were compared by cross-validation. The better one was adopted to extract a single marker, the general chronic condition (GCC), to represent the body’s chronic conditions. Multivariate linear regression was performed to explore the relationship between GCC and RFs. In total, 1,656 postmenopausal females were included. A multi-layer autoencoder outperformed PCA in the dimensionality reduction performance. The extracted variable by autoencoder, GCC, was verified to be representative of three chronic conditions (AUC for patoglycemia, hypertension and dyslipidemia were 0.844, 0.824 and 0.805 respectively). Linear regression showed that earlier age at menarche (OR = 0.9976) and shorter reproductive life span (OR = 0.9895) were associated with higher GCC. Autoencoder performed well in the dimensionality reduction of clinical metabolic biomarkers. Due to high accessibility and effectiveness, RFs have potential to be included in screening tools for general chronic conditions and could enhance current screening guidelines. Nature Publishing Group UK 2020-02-18 /pmc/articles/PMC7028713/ /pubmed/32071372 http://dx.doi.org/10.1038/s41598-020-59825-3 Text en © The Author(s) 2020 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 Tian, Siyu Dong, Weinan Chan, Ka Lung Leng, Xinyi Bedford, Laura Elizabeth Liu, Jia Screening for chronic conditions with reproductive factors using a machine learning based approach |
title | Screening for chronic conditions with reproductive factors using a machine learning based approach |
title_full | Screening for chronic conditions with reproductive factors using a machine learning based approach |
title_fullStr | Screening for chronic conditions with reproductive factors using a machine learning based approach |
title_full_unstemmed | Screening for chronic conditions with reproductive factors using a machine learning based approach |
title_short | Screening for chronic conditions with reproductive factors using a machine learning based approach |
title_sort | screening for chronic conditions with reproductive factors using a machine learning based approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028713/ https://www.ncbi.nlm.nih.gov/pubmed/32071372 http://dx.doi.org/10.1038/s41598-020-59825-3 |
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