Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

Danesh, Farshid; Dastani, Meisam; Ghorbani, Mohammad

doi:10.1108/lht-09-2020-0216

Cited by 17 publications

(19 citation statements)

References 62 publications

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“…ese topics were "structure and proteomics," "cell signaling and immune response," "clinical presentation and detection," "gene sequence and genomics," "diagnosis tests," "vaccine and immune response and outbreak," "epidemiology and transmission," and "gastrointestinal tissue" [62].…”

Section: Discussionmentioning

confidence: 99%

Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

Dastani

Danesh

2021

Scientific Programming

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COVID-19 is a threat to the lives of people all over the world. As a result of the new and unknown nature of COVID-19, much research has been conducted recently. In order to increase and enhance the growth rate of Iranian publications on COVID-19, this article aims to analyze these publications in LitCovid to identify the topical and content structure and topic modeling of scientific publications in the mentioned subject area. The present article is applied research performed by using an analytical approach as well as text mining techniques. The statistical population is all the publications of Iranian researchers in LitCovid. Latent Dirichlet Allocation (LDA) and Python were used to analyze the data and implement text mining and topic modeling algorithms. Data analysis shows that the percentage of Iranian publications in the eight topical groups in LitCovid is as follows: prevention (39.57%), treatment (18.99%), diagnosis (18.99%), forecasting (7.83%), case report (6.52%), mechanism (3.91%), transmission (3.62%), and general (0.58%). The results indicate that patient, pandemic, outbreak, case, Iranian, model, care, health, coronavirus, and disease are the most important words in the publications of Iranian researchers in LitCovid. Six topics for prevention; four topics for treatment and case report and forecasting; three topics for diagnosis, mechanism, and transmission in general have been obtained by implementing the topic modeling algorithm. Most of the Iranian publications in LitCovid are related to the topic “pandemic status,” with 22.47% in the prevention category, and the lowest number of publications is related to the topic “environment,” with 11.11% in the transmission category. The present study indicates a better understanding of essential and strategic issues of Iranian publications in LitCovid. The results reveal that many Iranian studies on COVID-19 were primarily on the issues related to prevention, management, and control. These findings provided a structured and research-based viewpoint of COVID-19 in Iran to guide researchers and policymakers.

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Section: Discussionmentioning

confidence: 99%

Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

Dastani

Danesh

2021

Scientific Programming

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“…Readers may also be interested in some related articles in Part I of our special issue. Danesh et al (2021) reported the topic modelling of the global coronavirus publications in the last 50 years with applied text mining and Latent Dirichlet allocation. Saab et al (2021) developed a deterministic model that quantifies previously adopted preventive measures driven by the reported number of deaths in Italy and India and used it to derive the optimal exiting policy using the inverse dynamics of the model.…”

Section: Research Funding and Bibliometric Researchmentioning

confidence: 99%

Guest editorial: COVID-19 Pandemic and Health Informatics Part 2

Huang

Paulino

et al. 2022

LHT

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“…Therefore, the number of topics should be determined logically, with thematic expert advice, and according to the extent of the topic matter (32). The LDA algorithm also determines the optimal number of topics, the frequency distribution of documents in the selected topics, and the keywords list related to each topic; however, it cannot do automatic labeling, and hence, topic labels are defined and specified non-automatically (33). Therefore, the topics resulting from the LDA algorithm are labeled and interpreted using each topic's most important words and articles.…”

Section: Topic Modelingmentioning

confidence: 99%

Identifying the Trends of Global Publications in Health Information Technology Using Text-mining Techniques

et al. 2022

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Background: Due to the increased publication of articles in various scientific fields, analyzing the published topics in specialized journals is important and necessary. Objectives: This research has identified the published topics in global publications in the health information technology (HIT) field. Methods: This study analyzed articles in the field of HIT using text-mining techniques. For this purpose, 162,994 documents were extracted from PubMed and Scopus databases from 2000 to 2019 using the appropriate search strategy. Text mining techniques and the Latent Dirichlet Allocation (LDA) topic modeling algorithm were used to identify the published topics. Python programming language has also been used to run text-mining algorithms. Results: This study categorized the subject of HIT-related published articles into 16 topics, the most important of which were Telemedicine and telehealth, Adoption of HIT, Radiotherapy planning techniques, Medical image analysis, and Evidence-based medicine. Conclusions: The results of the trends of subjects of HIT-related published articles represented the thematic extent and the interdisciplinary nature of this field. The publication of various topics in this scientific field has shown a growing trend in recent years.

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Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

Cited by 17 publications

References 62 publications

Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

Iranian COVID-19 Publications in LitCovid: Text Mining and Topic Modeling

Guest editorial: COVID-19 Pandemic and Health Informatics Part 2

Identifying the Trends of Global Publications in Health Information Technology Using Text-mining Techniques

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