Evidence map›Paper›PMID 35686200›Full record

ArticleClinical and translational allergy2022

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

Ariane Duverdier, Adnan Custovic, Reiko J Tanaka

Abstract read
In one paragraph

Article in Clinical and translational allergy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ariane DuverdierDepartment of Computing Imperial College London London UK.ORCID https://orcid.org/0000-0003-0132-3525
Adnan CustovicNational Heart and Lung Institute Imperial College London London UK.ORCID https://orcid.org/0000-0001-5218-7071
Reiko J TanakaDepartment of Bioengineering Imperial College London London UK.ORCID https://orcid.org/0000-0002-0769-9382

Funding

Medical Research Council MR/K002449/1Medical Research Council MR/K002449/2Medical Research Council MR/S002359/1Medical Research Council MR/S025340/1Medical Research Council MR/T031565/1Medical Research Council MR/W028352/1
6 · The paper itself

Abstract

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.

Indexed as

artificial intelligenceatopic dermatitisbibliometric analysisstatistics

Identifiers

PMID35686200
PMCPMC9172212

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.