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Review
. 2024 Jun 19;12(6):1237.
doi: 10.3390/microorganisms12061237.

Canine Bacterial Endocarditis: A Text Mining and Topics Modeling Analysis as an Approach for a Systematic Review

Annalisa Previti  1 , Vito Biondi  1 , Annamaria Passantino  1 , Mehmet Erman Or  2 , Michela Pugliese  1
Affiliations
Review

Canine Bacterial Endocarditis: A Text Mining and Topics Modeling Analysis as an Approach for a Systematic Review

Annalisa Previti et al. Microorganisms. .

Abstract

Bacterial endocarditis (BE) is a severe infection of the endocardium and cardiac valves caused by bacterial agents in dogs. Diagnosis of endocarditis is challenging due to the variety of clinical presentations and lack of definitive diagnostic tests in its early stages. This study aims to provide a research literature analysis on BE in dogs based on text mining (TM) and topic analysis (TA) identifying dominant topics, summarizing their temporal trend, and highlighting any possible research gaps. A literature search was performed utilizing the Scopus® database, employing keywords pertaining to BE to analyze papers published in English from 1990 to 2023. The investigation followed a systematic approach based on the PRISMA guidelines. A total of 86 records were selected for analysis following screening procedures and underwent descriptive statistics, TM, and TA. The findings revealed that the number of records published per year has increased in 2007 and 2021. TM identified the words with the highest term frequency-inverse document frequency (TF-IDF), and TA highlighted the main research areas, in the following order: causative agents, clinical findings and predisposing factors, case reports on endocarditis, outcomes and biomarkers, and infective endocarditis and bacterial isolation. The study confirms the increasing interest in BE but shows where further studies are needed.

Keywords: bacterial endocarditis; bartonella; machine learning; text mining; topic analysis.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow diagram of the review process according to the PRISMA statement.
Figure 2
Figure 2
The total number of records published per year between 1950 and 2023.
Figure 3
Figure 3
Five journals more representative for the publication of articles related to the topic.
Figure 4
Figure 4
The histogram reports in the root the most frequently words used based on the weighting system (TF-IDF ≥ 0.8).
Figure 5
Figure 5
The word cloud reports the words more frequently used. The font size corresponds to the TF-IDF value of each word.
Figure 6
Figure 6
Topics numbered from 1 to 5 according to the cumulative probabilities (CPs), as well as the first 10 words for each topic numbered.
Figure 7
Figure 7
Distribution of the articles within the five topics from 1950 to 2024.
See this image and copyright information in PMC

References

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