Detecting Latent Topics and Trends in Global Publications on Brucellosis Disease Using Text Mining

Unsupervised learning and natural language processing highlight research trends in a superbug

Introduction

Antibiotic-resistant Acinetobacter baumannii is a very important nosocomial pathogen worldwide. Thousands of studies have been conducted about this pathogen. However, there has not been any attempt to use all this information to highlight the research trends concerning this pathogen.

Methods

Here we use unsupervised learning and natural language processing (NLP), two areas of Artificial Intelligence, to analyse the most extensive database of articles created (5,500+ articles, from 851 different journals, published over 3 decades).

Results

K-means clustering found 113 theme clusters and these were defined with representative terms automatically obtained with topic modelling, summarising different research areas. The biggest clusters, all with over 100 articles, are biased toward multidrug resistance, carbapenem resistance, clinical treatment, and nosocomial infections. However, we also found that some research areas, such as ecology and non-human infections, have received very little attention. This approach allowed us to study research themes over time unveiling those of recent interest, such as the use of Cefiderocol (a recently approved antibiotic) against A. baumannii.

Discussion

In a broader context, our results show that unsupervised learning, NLP and topic modelling can be used to describe and analyse the research themes for important infectious diseases. This strategy should be very useful to analyse other ESKAPE pathogens or any other pathogens relevant to Public Health.

Topic Analysis and Mapping of Tuberculosis Research Using Text Mining and Co-Word Analysis

Tuberculosis is still one of the most severe progressive diseases; it severely limits the social and economic development of many countries. In the present study, the topic trend of scientific publications on tuberculosis has been examined using text mining techniques and co-word analysis with an analytical approach. The statistical population of the study is all global publications related to tuberculosis. In order to extract the data, the Scopus citation database was used for the period 1900 to 2022. The main keywords for the search strategy were chosen through consultation with thematic specialists and using MESH. Python programming language and VOSviewer software were applied to analyze data. The results showed four main topics as follows: “Clinical symptoms” (41.8%), “Diagnosis and treatment” (28.1%), “Bacterial structure, pathogenicity and genetics” (22.3%), and “Prevention” (7.84%). The results of this study can be helpful in the decision of this organization and knowledge of the process of studies on tuberculosis and investment and development of programs and guidelines against this disease.