Cargando…
Future world cancer death rate prediction
Cancer is a worldwide illness that causes significant morbidity and death and imposes an immense cost on global public health. Modelling such a phenomenon is complex because of the non-stationarity and complexity of cancer waves. Apply modern novel statistical methods directly to raw clinical data....
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9822976/ https://www.ncbi.nlm.nih.gov/pubmed/36609490 http://dx.doi.org/10.1038/s41598-023-27547-x |
_version_ | 1784866054908936192 |
---|---|
author | Gaidai, Oleg Yan, Ping Xing, Yihan |
author_facet | Gaidai, Oleg Yan, Ping Xing, Yihan |
author_sort | Gaidai, Oleg |
collection | PubMed |
description | Cancer is a worldwide illness that causes significant morbidity and death and imposes an immense cost on global public health. Modelling such a phenomenon is complex because of the non-stationarity and complexity of cancer waves. Apply modern novel statistical methods directly to raw clinical data. To estimate extreme cancer death rate likelihood at any period in any location of interest. Traditional statistical methodologies that deal with temporal observations of multi-regional processes cannot adequately deal with substantial regional dimensionality and cross-correlation of various regional variables. Setting: multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of cancer, it is challenging to model such a phenomenon. This paper offers a unique bio-system dependability technique suited for multi-regional environmental and health systems. When monitored over a significant period, it yields a reliable long-term projection of the chance of an exceptional cancer mortality rate. Traditional statistical approaches dealing with temporal observations of multi-regional processes cannot effectively deal with large regional dimensionality and cross-correlation between multiple regional data. The provided approach may be employed in numerous public health applications, depending on their clinical survey data. |
format | Online Article Text |
id | pubmed-9822976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98229762023-01-08 Future world cancer death rate prediction Gaidai, Oleg Yan, Ping Xing, Yihan Sci Rep Article Cancer is a worldwide illness that causes significant morbidity and death and imposes an immense cost on global public health. Modelling such a phenomenon is complex because of the non-stationarity and complexity of cancer waves. Apply modern novel statistical methods directly to raw clinical data. To estimate extreme cancer death rate likelihood at any period in any location of interest. Traditional statistical methodologies that deal with temporal observations of multi-regional processes cannot adequately deal with substantial regional dimensionality and cross-correlation of various regional variables. Setting: multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of cancer, it is challenging to model such a phenomenon. This paper offers a unique bio-system dependability technique suited for multi-regional environmental and health systems. When monitored over a significant period, it yields a reliable long-term projection of the chance of an exceptional cancer mortality rate. Traditional statistical approaches dealing with temporal observations of multi-regional processes cannot effectively deal with large regional dimensionality and cross-correlation between multiple regional data. The provided approach may be employed in numerous public health applications, depending on their clinical survey data. Nature Publishing Group UK 2023-01-06 /pmc/articles/PMC9822976/ /pubmed/36609490 http://dx.doi.org/10.1038/s41598-023-27547-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gaidai, Oleg Yan, Ping Xing, Yihan Future world cancer death rate prediction |
title | Future world cancer death rate prediction |
title_full | Future world cancer death rate prediction |
title_fullStr | Future world cancer death rate prediction |
title_full_unstemmed | Future world cancer death rate prediction |
title_short | Future world cancer death rate prediction |
title_sort | future world cancer death rate prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9822976/ https://www.ncbi.nlm.nih.gov/pubmed/36609490 http://dx.doi.org/10.1038/s41598-023-27547-x |
work_keys_str_mv | AT gaidaioleg futureworldcancerdeathrateprediction AT yanping futureworldcancerdeathrateprediction AT xingyihan futureworldcancerdeathrateprediction |