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Systematic literature review on data-driven research on atopic dermatitis and eczema

This repository contains the code developed for the article by Duverdier et al. (2022), "Data-driven research on eczema: Systematic characterization of the field and recommendations for the future", published in Clinical and Translational Allergy.

This project performs a systematic literature review on data-driven atopic dermatitis (AD) and eczema research.

  1. The literature search was conducted on the SCOPUS database, retrieving all documents that apply multivariate statistics (MS), machine learning and artificial intelligence (ML&AI), and/or Bayesian statistics (BS) methods to AD and eczema research.
  2. A bibliometric analysis was conducted on the corpus of documents to highlight the publication trends and conceptual knowledge structure of the field of research.
  3. Topic modelling, using the Latent Dirichlet Allocation (LDA) algorithm was applied on the corpus of documents to retrieve the key topics present within the literature.

The code is written in R version 4.0.4.

Files

The datasets required for this project are found in the folder data. These csv files contain the downloaded SCOPUS entries according to the methodology (MS, ML&AI, or BS) and the term (AD or eczema). The data can be loaded and pre-processed using the functions in 001_data_import.R.

The bibliometric analysis is performed in 002_bibliometric_analysis.R using the bibliometrix R package introduced in Aria & Cuccurullo (2017).

LDA is applied to the collection of documents in 003_lda.R using the topicmodels R package implementation.

All functions and examples of how to run them are provided in the files.

Libraries

The project is created with the following libraries and their versions:

  • bibliometrix 3.0.4
  • tm 0.7-8
  • topicmodels 0.2-12
  • topicdoc 0.1.0
  • RVenn 1.1.0
  • ggplot2 3.3.3
  • dplyr 1.0.4
  • tidyr 1.1.2
  • plyr 1.8.6
  • stringr 1.4.0
  • textmineR 3.0.4
  • tidytext 0.3.1
  • wordcloud 2.6
  • reshape2 1.4.4
  • pheatmap 1.0.12

All packages used in this project can be installed using install.packages("name_of_package").

License

The open source version of this project is licensed under the GPLv3 license, which can be seen in the LICENSE file.