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Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning

Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for ou...

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Detalles Bibliográficos
Autor principal: Luo, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8215338/
https://www.ncbi.nlm.nih.gov/pubmed/34164615
http://dx.doi.org/10.3389/frai.2021.627369
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author Luo, Wei
author_facet Luo, Wei
author_sort Luo, Wei
collection PubMed
description Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use.
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spelling pubmed-82153382021-06-22 Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning Luo, Wei Front Artif Intell Artificial Intelligence Cervical cancer is a very common and severe disease in women worldwide. Accurate prediction of its clinical outcomes will help adjust or optimize the treatment of cervical cancer and benefit the patients. Statistical models, various types of medical images, and machine learning have been used for outcome prediction and obtained promising results. Compared to conventional statistical models, machine learning has demonstrated advantages in dealing with the complexity in large-scale data and discovering prognostic factors. It has great potential in clinical application and improving cervical cancer management. However, the limitations of prediction studies and prediction models including simplification, insufficient data, overfitting and lack of interpretability, indicate that more work is needed to make clinical outcome prediction more accurate, more reliable, and more practical for clinical use. Frontiers Media S.A. 2021-06-07 /pmc/articles/PMC8215338/ /pubmed/34164615 http://dx.doi.org/10.3389/frai.2021.627369 Text en Copyright © 2021 Luo. 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 Artificial Intelligence
Luo, Wei
Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
title Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
title_full Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
title_fullStr Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
title_full_unstemmed Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
title_short Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning
title_sort predicting cervical cancer outcomes: statistics, images, and machine learning
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8215338/
https://www.ncbi.nlm.nih.gov/pubmed/34164615
http://dx.doi.org/10.3389/frai.2021.627369
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