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Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future

BACKGROUND: The past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the appl...

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Autores principales: Duverdier, Ariane, Custovic, Adnan, Tanaka, Reiko J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9172212/
https://www.ncbi.nlm.nih.gov/pubmed/35686200
http://dx.doi.org/10.1002/clt2.12170
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author Duverdier, Ariane
Custovic, Adnan
Tanaka, Reiko J.
author_facet Duverdier, Ariane
Custovic, Adnan
Tanaka, Reiko J.
author_sort Duverdier, Ariane
collection PubMed
description BACKGROUND: The past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the application of these methods. METHODS: We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning‐ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. RESULTS: Five key themes of data‐driven research on AD and eczema were identified: (1) allergic co‐morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co‐morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers. CONCLUSIONS: Research areas that could benefit from the application of data‐driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research.
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spelling pubmed-91722122022-06-08 Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future Duverdier, Ariane Custovic, Adnan Tanaka, Reiko J. Clin Transl Allergy Original Article BACKGROUND: The past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the application of these methods. METHODS: We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning‐ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. RESULTS: Five key themes of data‐driven research on AD and eczema were identified: (1) allergic co‐morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co‐morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers. CONCLUSIONS: Research areas that could benefit from the application of data‐driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research. John Wiley and Sons Inc. 2022-06-07 /pmc/articles/PMC9172212/ /pubmed/35686200 http://dx.doi.org/10.1002/clt2.12170 Text en © 2022 The Authors. Clinical and Translational Allergy published by John Wiley & Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Duverdier, Ariane
Custovic, Adnan
Tanaka, Reiko J.
Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future
title Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future
title_full Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future
title_fullStr Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future
title_full_unstemmed Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future
title_short Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future
title_sort data‐driven research on eczema: systematic characterization of the field and recommendations for the future
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9172212/
https://www.ncbi.nlm.nih.gov/pubmed/35686200
http://dx.doi.org/10.1002/clt2.12170
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