Duverdier Ariane, Custovic Adnan, Tanaka Reiko J
Department of Computing Imperial College London London UK.
Department of Bioengineering Imperial College London London UK.
Clin Transl Allergy. 2022 Jun 7;12(6):e12170. doi: 10.1002/clt2.12170. eCollection 2022 Jun.
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.
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.
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.
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.
在过去十年中，运用现代数据驱动方法研究特应性皮炎（AD）/湿疹的情况大幅增加。本研究的目的是总结数据驱动的AD研究的过去与未来，并确定该领域中可从这些方法的应用中受益的领域。
我们从SCOPUS数据库中检索了过去50年将多元统计（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。
确定了AD和湿疹数据驱动研究的五个关键主题：（1）过敏性共病，（2）图像分析与分类，（3）分解，（4）生活质量与治疗反应，以及（5）风险因素与患病率。ML&AI方法应用于研究AD/湿疹的生活质量、患病率、风险因素、过敏性共病和分解，但很少用于治疗研究。MS在各主题之间均匀使用，特别是在风险因素和患病率的研究中。BS集中在三个关键主题：治疗、风险因素和过敏。AD或湿疹术语的使用并不统一，应用ML&AI方法的研究更常使用湿疹一词。在MS中，使用聚类和因子分析的论文通常仅用AD一词来识别。相比之下，使用逻辑回归和潜在类别/转换模型的则是“湿疹”论文。
可从数据驱动方法的应用中受益的研究领域包括该疾病的发病机制及相关风险因素的研究、将其分解为经过验证的亚型、个性化的严重程度管理和预后。我们强调BS是AD和湿疹研究中一种新的且有前景的方法。
