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The soft computing-based approach to investigate allergic diseases: a systematic review
BACKGROUND: Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; howev...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390370/ https://www.ncbi.nlm.nih.gov/pubmed/28413358 http://dx.doi.org/10.1186/s12948-017-0066-3 |
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author | Tartarisco, Gennaro Tonacci, Alessandro Minciullo, Paola Lucia Billeci, Lucia Pioggia, Giovanni Incorvaia, Cristoforo Gangemi, Sebastiano |
author_facet | Tartarisco, Gennaro Tonacci, Alessandro Minciullo, Paola Lucia Billeci, Lucia Pioggia, Giovanni Incorvaia, Cristoforo Gangemi, Sebastiano |
author_sort | Tartarisco, Gennaro |
collection | PubMed |
description | BACKGROUND: Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field. OBJECTIVE: The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases. METHODS: The review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016. RESULTS: The review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause–effect relationships, which make it difficult to assess the evolution of disease. CONCLUSIONS: Although most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well. |
format | Online Article Text |
id | pubmed-5390370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53903702017-04-14 The soft computing-based approach to investigate allergic diseases: a systematic review Tartarisco, Gennaro Tonacci, Alessandro Minciullo, Paola Lucia Billeci, Lucia Pioggia, Giovanni Incorvaia, Cristoforo Gangemi, Sebastiano Clin Mol Allergy Research BACKGROUND: Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field. OBJECTIVE: The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases. METHODS: The review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016. RESULTS: The review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause–effect relationships, which make it difficult to assess the evolution of disease. CONCLUSIONS: Although most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well. BioMed Central 2017-04-13 /pmc/articles/PMC5390370/ /pubmed/28413358 http://dx.doi.org/10.1186/s12948-017-0066-3 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Tartarisco, Gennaro Tonacci, Alessandro Minciullo, Paola Lucia Billeci, Lucia Pioggia, Giovanni Incorvaia, Cristoforo Gangemi, Sebastiano The soft computing-based approach to investigate allergic diseases: a systematic review |
title | The soft computing-based approach to investigate allergic diseases: a systematic review |
title_full | The soft computing-based approach to investigate allergic diseases: a systematic review |
title_fullStr | The soft computing-based approach to investigate allergic diseases: a systematic review |
title_full_unstemmed | The soft computing-based approach to investigate allergic diseases: a systematic review |
title_short | The soft computing-based approach to investigate allergic diseases: a systematic review |
title_sort | soft computing-based approach to investigate allergic diseases: a systematic review |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390370/ https://www.ncbi.nlm.nih.gov/pubmed/28413358 http://dx.doi.org/10.1186/s12948-017-0066-3 |
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