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Popular deep learning algorithms for disease prediction: a review
Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This pape...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469816/ https://www.ncbi.nlm.nih.gov/pubmed/36120180 http://dx.doi.org/10.1007/s10586-022-03707-y |
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author | Yu, Zengchen Wang, Ke Wan, Zhibo Xie, Shuxuan Lv, Zhihan |
author_facet | Yu, Zengchen Wang, Ke Wan, Zhibo Xie, Shuxuan Lv, Zhihan |
author_sort | Yu, Zengchen |
collection | PubMed |
description | Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research. |
format | Online Article Text |
id | pubmed-9469816 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-94698162022-09-14 Popular deep learning algorithms for disease prediction: a review Yu, Zengchen Wang, Ke Wan, Zhibo Xie, Shuxuan Lv, Zhihan Cluster Comput Article Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research. Springer US 2022-09-13 2023 /pmc/articles/PMC9469816/ /pubmed/36120180 http://dx.doi.org/10.1007/s10586-022-03707-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Yu, Zengchen Wang, Ke Wan, Zhibo Xie, Shuxuan Lv, Zhihan Popular deep learning algorithms for disease prediction: a review |
title | Popular deep learning algorithms for disease prediction: a review |
title_full | Popular deep learning algorithms for disease prediction: a review |
title_fullStr | Popular deep learning algorithms for disease prediction: a review |
title_full_unstemmed | Popular deep learning algorithms for disease prediction: a review |
title_short | Popular deep learning algorithms for disease prediction: a review |
title_sort | popular deep learning algorithms for disease prediction: a review |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469816/ https://www.ncbi.nlm.nih.gov/pubmed/36120180 http://dx.doi.org/10.1007/s10586-022-03707-y |
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