What's Behind CRM Research? A Bibliometric Analysis of Publications in the CRM Research Field

Araújo, Cíntia Cristina Silva de; Pedron, Cristiane Drebes; Picoto, Winnie Ng

doi:10.1080/15332667.2018.1440139

Cited by 21 publications

(25 citation statements)

References 20 publications

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“…A network of countries sharing co-authorships, the author keyword co-occurrence network and countries network were created by VOSviewer (version 1.6.8, Leiden University, Leiden, Netherlands). As for content analysis of the abstracts, we applied exploratory factor analysis to identify research domains emerging from all content of the abstract; loadings of 0.4 [18]. Jaccard’s similarity index was utilized to identify research topics or terms most frequently co-occurring with each other [19].…”

Section: Methodsmentioning

confidence: 99%

Depression among Patients with HIV/AIDS: Research Development and Effective Interventions (GAPRESEARCH)

Tran

et al. 2019

IJERPH

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Depression in people living with HIV (PLWH) has become an urgent issue and has attracted the attention of both physicians and epidemiologists. Currently, 39% of HIV patients are reported to suffer from depression. This population is more likely to experience worsening disease states and, thus, poorer health outcomes. In this study, we analyzed research growth and current understandings of depression among HIV-infected individuals. The number of papers and their impacts have been considerably grown in recent years, and a total of 4872 publications published from 1990–2017 were retrieved from the Web of Science database. Research landscapes related to this research field include risk behaviors and attributable causes of depression in HIV population, effects of depression on health outcomes of PLWH, and interventions and health services for these particular subjects. We identified a lack of empirical studies in countries where PLWH face a high risk of depression, and a modest level of interest in biomedical research. By demonstrating these research patterns, highlighting the research gaps and putting forward implications, this study provides a basis for future studies and interventions in addressing the critical issue of HIV epidemics.

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Section: Methodsmentioning

confidence: 99%

Depression among Patients with HIV/AIDS: Research Development and Effective Interventions (GAPRESEARCH)

Tran

et al. 2019

IJERPH

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“…VOSviewer (version 1.6.8, Center for Science and Technology, Leiden University) was used to establish a co-occurrence network and a countries network. The principles of underlying algorithms used by the software for clustering have been documented elsewhere [11-14] For content analysis of the abstracts, we applied the exploratory factor analysis to identify research domains emerging from all content of the abstracts, loadings of 0.4 [15]. The Jaccard similarity index was utilized to identify research topics or terms most frequently co-occurring with each other [16].…”

Section: Methodsmentioning

confidence: 99%

Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis

Tran¹,

Latkin²,

Sharafeldin³

et al. 2019

JMIR Med Inform

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Background Artificial intelligence (AI)–based therapeutics, devices, and systems are vital innovations in cancer control; particularly, they allow for diagnosis, screening, precise estimation of survival, informing therapy selection, and scaling up treatment services in a timely manner. Objective The aim of this study was to analyze the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research. Methods An exploratory factor analysis was conducted to identify research domains emerging from abstract contents. The Jaccard similarity index was utilized to identify the most frequently co-occurring terms. Latent Dirichlet Allocation was used for classifying papers into corresponding topics. Results From 1991 to 2018, the number of studies examining the application of AI in cancer care has grown to 3555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volume of publications include (1) machine learning, (2) comparative effectiveness evaluation of AI-assisted medical therapies, and (3) AI-based prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life, and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches are largely driven by machine learning, artificial neural networks, and AI in various clinical practices. Conclusions The research landscapes show that the development of AI in cancer care is focused on not only improving prediction in cancer screening and AI-assisted therapeutics but also on improving other corresponding areas such as precision and personalized medicine and patient-reported outcomes.

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“…VOSviewer software (version 1.6.8, Center for Science and Technology, Leiden University, the Netherlands) was used to extract the authors’ keywords and establish a co-occurrence network of the most common ones. For further content analysis of the abstracts, we applied exploratory factor analysis, which is a statistical method uncovering the underlying association between a variable set, to identify co-occurring terms and, thus, uncover the major research domains from all content of the abstracts [29]. Latent Dirichlet Allocation (LDA), which is a generative probabilistic model for collections of discrete data including text corpora, was used for classifying papers into corresponding topics [30,31,32,33,34].…”

Section: Methodsmentioning

confidence: 99%

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

Tran

McIntyre

Latkin

et al. 2019

IJERPH

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Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.

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What's Behind CRM Research? A Bibliometric Analysis of Publications in the CRM Research Field

Cited by 21 publications

References 20 publications

Depression among Patients with HIV/AIDS: Research Development and Effective Interventions (GAPRESEARCH)

Depression among Patients with HIV/AIDS: Research Development and Effective Interventions (GAPRESEARCH)

Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis

The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

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