A roadmap toward the automatic composition of systematic literature reviews

Júnior, Eugênio Monteiro da Silva; Dutra, Moisés Lima

doi:10.47909/ijsmc.52

Cited by 24 publications

(7 citation statements)

References 54 publications

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“…The Scopus database was used as a data source. The bibliometric review focuses on different methods to analyze quantitative, qualitative, and structural changes in scientific research and the set of publications related to a specific topic (44) (45) (46). For the document search, an advanced search was performed using the following search strategy: OR AFFILCOUNTRY(South Georgia and the South Sandwich Islands)) Studies from the last ten years (2013-2022) were analyzed without language limitations.…”

Section: Methodsmentioning

confidence: 99%

Telemedicine in Latin America: a bibliometric analysis

Carpio-Delgado,

Romero-Carazas,

Pino-Espinoza

et al. 2023

EAI Endorsed Trans Perv Health Tech

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Introduction: Telemedicine revolutionizes health care by removing geographic barriers and improving access. Although it faces challenges such as privacy and equity of access, bibliometric studies are crucial to understanding its impact and guiding future research. Methods: The study used a descriptive bibliometric methodology based on the Scopus database to analyze telemedicine research in Latin America over the last ten years, resulting in 2105 academic articles. Tools such as SciVal and VOSviewer were used to perform quantitative and visual analyses of the publications, including creating bibliometric maps. Result: From 2013-2022, 2105 academic articles on telemedicine were published in Latin America, with a significant impact on the health field. A particular focus is observed on topics such as psychological support, COVID-19, imaging diagnosis and cancer treatment, highlighting the relevance of telemedicine in these contexts. In addition, international collaboration was associated with a more significant impact. Brazil produced articles, and the importance of collaboration between academia and the corporate sector in this field was highlighted. Conclusions: Telemedicine has grown in Latin America, especially during the pandemic, offering benefits such as psychological support and expedited diagnosis and treatment; however, it faces challenges such as a lack of equitable access to technology and concerns about data privacy. Brazil leads scientific production in this field.

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Section: Methodsmentioning

confidence: 99%

Telemedicine in Latin America: a bibliometric analysis

Carpio-Delgado,

Romero-Carazas,

Pino-Espinoza

et al. 2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…A three-phase protocol was used for the thematic analysis, in order to triangulate the data and the perspectives of each researcher and to achieve an adequate integration and discussion of the main results. 116 We used an adaptation of the methodology for conducting thematic analysis in interdisciplinary health teams proposed by Saunders et al and the procedures were reviewed in terms of similar studies.…”

Section: Scoping Reviewmentioning

confidence: 99%

Internet of Things and Health: A literature review based on Mixed Method

Cano,

Castillo,

Gamboa

et al. 2024

EAI Endorsed Trans IoT

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The integration of technological advances into health sciences has promoted their development, but also generated setbacks and difficulties for digital transformation. In different areas, technology has modified the processes of diagnosis, teaching and learning, treatment and monitoring, which is why the study of new technologies and the models that support their introduction is essential. Internet of Things is one of these models, which, in turn, includes different models, devices and applications. Due to its breadth of exploitation options and benefits, in the health area this concept has been adopted and particularized as the Internet of Medical Things. With the purpose of achieving an approximation to the main trends and characteristics, a literature review study was conducted, based on mixed methods. Two studies were carried out with a sequential strategy, the first being bibliometric and the second a scoping review. The main results allowed us to describe the main trends in terms of bibliometric indicators, a thematic analysis in terms of areas, populations, benefits and limitations. It is concluded that there is a need for new interdisciplinary studies and lines for future research are presented.

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“…A theoretical justification for the superior performance of RandomForest lies in its nature as a Machine Learning ensemble [60][61][62][63][64][65] , which are techniques that combine multiple individual models to improve predictive capability and system robustness 66 . It is characterized by creating a set of decision trees, each representing an individual model in the ensemble 60,[67][68][69][70][71][72] .…”

Section: Eai Endorsed Transactions On Scalable Information Systemsmentioning

confidence: 99%

Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review

Andrade-Girón,

Sandivar-Rosas,

Marín-Rodriguez

et al. 2023

ICST Transactions on Scalable Information Systems

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Student dropout is one of the most complex challenges facing the education system worldwide. In order to evaluate the success of Machine Learning and Deep Learning algorithms in predicting student dropout, a systematic review was conducted. The search was carried out in several electronic bibliographic databases, including Scopus, IEEE, and Web of Science, covering up to June 2023, having 246 articles as search reports. Exclusion criteria, such as review articles, editorials, letters, and comments, were established. The final review included 23 studies in which performance metrics such as accuracy/precision, sensitivity/recall, specificity, and area under the curve (AUC) were evaluated. In addition, aspects related to study modality, training, testing strategy, cross-validation, and confounding matrix were considered. The review results revealed that the most used Machine Learning algorithm was Random Forest, present in 21.73% of the studies; this algorithm obtained an accuracy of 99% in the prediction of student dropout, higher than all the algorithms used in the total number of studies reviewed.

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A roadmap toward the automatic composition of systematic literature reviews

Cited by 24 publications

References 54 publications

Telemedicine in Latin America: a bibliometric analysis

Telemedicine in Latin America: a bibliometric analysis

Internet of Things and Health: A literature review based on Mixed Method

Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review

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