Data-driven research on eczema: systematic characterization of the field and recommendations for the future
Data-driven research on eczema: systematic characterization of the field and recommendations for the future
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数据驱动的湿疹研究：该领域的系统特征和未来建议
DOI：
10.1101/2022.01.14.22269294
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Duverdier A
中科院分区：
文献类型：
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作者：
Duverdier A
在过去的十年中，采用现代数据驱动的方法研究特应性皮炎(AD)/湿疹的人数大幅增加。本研究的目的是总结数据驱动的AD研究的过去和未来，并确定将从这些方法的应用中受益的领域。方法从SCOPUS数据库中检索近50年来将多元统计（MS）、人工智能（AI，包括机器学习- ML）和贝叶斯统计（BS）应用于AD和湿疹研究的出版物。我们进行了文献计量分析，以突出该领域的出版趋势和概念知识结构，并应用主题建模来检索文献中的关键主题。结果确定了AD和湿疹数据驱动研究的五个关键主题：(1)过敏共发病；(2)图像分析和分类；(3)分类；(4)生活质量和治疗反应；(5)危险因素和患病率。ML&AI方法用于调查AD/湿疹的生活质量、患病率、危险因素、过敏共病和分解的研究，但很少用于治疗研究。在主题之间均匀地使用MS，特别是在危险因素和患病率的研究中。BS集中于三个关键主题：治疗、危险因素和过敏。AD或湿疹术语的使用并不统一，应用ML&AI方法的研究更经常使用术语湿疹。在MS中，使用聚类和因子分析的论文通常只被识别为AD一词。相比之下，那些使用逻辑回归和潜在类别/过渡模型的论文是“湿疹”论文。应用数据驱动的方法可以受益的研究领域包括疾病的发病机制和相关危险因素的研究，将其分解为有效的亚型，以及个性化的严重程度管理和预后。我们强调BS在AD和湿疹研究中是一个新的和有前途的方法。
BackgroundThe 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.MethodsWe 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.ResultsFive 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.ConclusionsResearch 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.