Cargando…

Early prediction of atherosclerosis diagnosis with medical ambient intelligence

Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Wen, Nie, Qilin, Sun, Yujie, Zou, Danrong, Tang, Jinmo, Wang, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398961/
https://www.ncbi.nlm.nih.gov/pubmed/37546535
http://dx.doi.org/10.3389/fphys.2023.1225636
_version_ 1785084146299699200
author Yang, Wen
Nie, Qilin
Sun, Yujie
Zou, Danrong
Tang, Jinmo
Wang, Min
author_facet Yang, Wen
Nie, Qilin
Sun, Yujie
Zou, Danrong
Tang, Jinmo
Wang, Min
author_sort Yang, Wen
collection PubMed
description Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the “SEcond-order Classifier (SEC)” is proposed to undertake the medical ambient computation task. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method’s diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future.
format Online
Article
Text
id pubmed-10398961
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-103989612023-08-04 Early prediction of atherosclerosis diagnosis with medical ambient intelligence Yang, Wen Nie, Qilin Sun, Yujie Zou, Danrong Tang, Jinmo Wang, Min Front Physiol Physiology Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the “SEcond-order Classifier (SEC)” is proposed to undertake the medical ambient computation task. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method’s diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future. Frontiers Media S.A. 2023-07-20 /pmc/articles/PMC10398961/ /pubmed/37546535 http://dx.doi.org/10.3389/fphys.2023.1225636 Text en Copyright © 2023 Yang, Nie, Sun, Zou, Tang and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Yang, Wen
Nie, Qilin
Sun, Yujie
Zou, Danrong
Tang, Jinmo
Wang, Min
Early prediction of atherosclerosis diagnosis with medical ambient intelligence
title Early prediction of atherosclerosis diagnosis with medical ambient intelligence
title_full Early prediction of atherosclerosis diagnosis with medical ambient intelligence
title_fullStr Early prediction of atherosclerosis diagnosis with medical ambient intelligence
title_full_unstemmed Early prediction of atherosclerosis diagnosis with medical ambient intelligence
title_short Early prediction of atherosclerosis diagnosis with medical ambient intelligence
title_sort early prediction of atherosclerosis diagnosis with medical ambient intelligence
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398961/
https://www.ncbi.nlm.nih.gov/pubmed/37546535
http://dx.doi.org/10.3389/fphys.2023.1225636
work_keys_str_mv AT yangwen earlypredictionofatherosclerosisdiagnosiswithmedicalambientintelligence
AT nieqilin earlypredictionofatherosclerosisdiagnosiswithmedicalambientintelligence
AT sunyujie earlypredictionofatherosclerosisdiagnosiswithmedicalambientintelligence
AT zoudanrong earlypredictionofatherosclerosisdiagnosiswithmedicalambientintelligence
AT tangjinmo earlypredictionofatherosclerosisdiagnosiswithmedicalambientintelligence
AT wangmin earlypredictionofatherosclerosisdiagnosiswithmedicalambientintelligence