Topics and trends in artificial intelligence assisted human brain research

A bibliometric perspective to the most cited diabetes articles

Aim

This bibliometric analysis aims to evaluate the characteristics and impact of the top 100 cited articles published under the title of diabetes mellitus.

Metods

We performed to define the most cited articles in diabetes research by using the Web of Science. The papers were analyzed in terms of their year of publication, journal of publication, authors, impact factor (IF), total citations number, the average number of citations per year, studies topic, and type.

Results

The number of citations ranged from 1519 to 17.298. They were published from 1987 to 2018. The most cited articles were published in the New England Journal of Medicine (n = 26), followed by Diabetes Care (n = 17) and Lancet (n = 9). The original scientific paper was the most popular article type (46%), followed by review article (36%). The generality studies' subject was about treatment (n = 22), followed by pathogenesis (n = 19), etiology and risk factors (n = 16), diagnosis, screening, classification (n = 15), epidemiology (n = 11), prevention (n = 11) and complications (n = 6). There was a correlation between the average number of citations per year (ACpY) and IF (p = < 0.010, r = 0.259), citations and ACpY (p = < 0.001, r = 0.646), citations and time (p = 0.008, r = 0.266).

Conclusion

This study showed that original scientific papers were the most-cited and more articles were published in influential journals. Articles on diabetes treatment and pathogenesis were popular topics. Future interventions should focus on the management and prevention of diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01199-0.

Thematic analysis of articles on artificial intelligence with spine trauma, vertebral metastasis, and osteoporosis using chord diagrams: A systematic review and meta-analysis

BACKGROUND

Spine trauma, vertebral metastases, and osteoporosis (SVO) can result in serious health problems. If the diagnosis of SVO is delayed, the prognosis may be deteriorated. The use of artificial intelligence (AI) is an essential method for minimizing the diagnostic errors associated with SVO. research achievements (RAs) of SVO on AI are required as a result of the greatest number of studies on AI solutions reported. The study aimed to: classify article themes using visualizations, illustrate the characteristics of SVO on AI recently, compare RAs of SVO on AI between entities (e.g., countries, institutes, departments, and authors), and determine whether the mean citations of keywords can be used to predict article citations.

METHODS

A total of 31 articles from SVO on AI (denoted by T31SVOAI) have been found in Web of Science since 2018. The dominant entities were analyzed using the CJAL score and the Y-index. Five visualizations were applied to report: the themes of T31SVOAI and their RAs in comparison for article entities and verification of the hypothesis that the mean citations of keywords can predict article citations, including: network diagrams, chord diagrams, dot plots, a Kano diagram, and radar plots.

RESULTS

There were five themes classified (osteoporosis, personalized medicine, fracture, deformity, and cervical spine) by a chord diagram. The dominant entities with the highest CJAL scores were the United States (22.05), the University of Pennsylvania (5.72), Radiology (6.12), and Nithin Kolanu (Australia) (9.88). The majority of articles were published in Bone, J. Bone Miner. Res., and Arch. Osteoporos., with an equal count (=3). There was a significant correlation between the number of article citations and the number of weighted keywords (F = 392.05; P < .0001).

CONCLUSION

A breakthrough was achieved by displaying the characteristics of T31SVOAI using the CJAL score, the Y-index, and the chord diagram. Weighted keywords can be used to predict article citations. The five visualizations employed in this study may be used in future bibliographical studies.

A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research

International Management is a vast and multidisciplinary research domain that is heavily influenced by several other disciplines, such as Economics, Organizational Theory and Strategic Management. Based on 28,973 research articles, this study aims to analyze the knowledge structure of the international management domain from 1920 to 2019. Using computational text-based topic modeling analysis, we trace the evolution of international management knowledge by examining the major academic topics/latent themes discussed in the field. The study also diachronically visualizes the variations in topic prevalence over time. Our methodology is akin to "inductive mapping" as it is neither biased by our position nor it is guided by assumptions related to the topics we expect to find. Results indicate the existence of a wide variety of important research foci in the domain of international management. These include, among others, strategic alliances formation, international entry modes, corporate social responsibility, cross-cultural consumer behavior, technological innovation and entrepreneurship. Results also show that some topics such as "financial risk and return on investment" and "corporate social responsibility" show a declining time trend, indicating that academic research focusing on such topics was more likely to be published early on and less so recently. On the other hand, other topics such as "Emerging (East) Asian nations" and "global mergers and acquisitions" show an increasing trend, indicating that more papers were published recently. Taken together, although our findings might reflect the breadth and depth of research in international management, they might also suggest that the bounds of this field are not well defined.

A scholarly network of AI research with an information science focus: Global North and Global South perspectives

This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research.

Machine and cognitive intelligence for human health: systematic review

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

The evolution and future of diabetic kidney disease research: a bibliometric analysis

BACKGROUND

Diabetic kidney disease (DKD) is one of the most important complications of diabetic mellitus. It is essential for nephrologists to understand the evolution and development trends of DKD.

METHODS

Based on the total cited numbers in the Web of Science Core Collection, which was searched through September 28th, 2020, we performed a bibliometric analysis of the top 100 most cited full-length original articles on the subject of DKD. The timespans, authors, contributions, subcategories, and topics of those 100 articles were analysed. In addition, the evolution of topics in DKD research was investigated.

RESULTS

There were 23,968 items under the subject of DKD in the Web of Science Core Collection. The top 100 cited articles, published from 1999 to 2017, were cited 38,855 times in total. Researchers from the USA contributed the most publications. The number of articles included in 'Experimental studies (EG)', 'Clinical studies (CS)', 'Epidemiological studies (ES)', and 'Pathological and pathophysiological studies (PP)' were 65, 26, 7, and 2, respectively. Among the 15 topics, the most popular topic is the renin-angiotensin-aldosterone system (RAAS), occurring in 26 articles, including 6 of the top 10 most cited articles. The evolution of topics reveals that the role of RAAS inhibitor is a continuous hotspot, and sodium-glucose cotransporter 2 (SGLT-2) inhibitor and glucagon-like peptide 1 (GLP-1) agonist are two renoprotective agents which represent novel therapeutic methods in DKD. In addition, the 26 clinical studies among the top 100 most cited articles were highlighted, as they help guide clinical practice to better serve patients.

CONCLUSIONS

This bibliometric analysis of the top 100 most cited articles revealed important studies, popular topics, and trends in DKD research to assist researchers in further understanding the subject.

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

Game-based self-regulated language learning: Theoretical analysis and bibliometrics

Game-based learning and self-regulated learning have long been valued as effective approaches to language education. However, little research has been conducted to investigate their integration, namely, game-based self-regulated language learning (GBSRLL). This study aims to conceptualise GBSRLL based on the combination of theoretical analysis, thematic evolution analysis, and social network analysis on the research articles in the fields of game-based language learning and self-regulated language learning. The results show that GBSRLL is a new interdisciplinary field emerging since the period from 2018 to 2019. Self-regulated learning strategies that can be performed in GBSRLL, the effects of GBSRLL on learners' affective states, and the features in GBSRLL were the prominent research topics in this field. Its theoretical foundation centres on the positive correlations between learner motivation, self-efficacy, and autonomy and the implementation of game-based learning and self-regulated learning. It is feasible to conduct GBSRLL due to the strong supportiveness of game mechanics for various phases and strategies of self-regulated learning. More contributions to this new interdisciplinary field are called for, especially from the aspects of the long-term effects of GBSRLL on academic performance and the useful tools and technologies for implementing GBSRLL.

Dose Correlation of Danggui and Chuanxiong Drug Pairs in the Chinese Medicine Prescription Based on the Copula Function

Dosage is essential for studying the compatibility and effectiveness of traditional Chinese medicine. Danggui and Chuanxiong are widely used in traditional Chinese medicine for ailments and treatment of various disorders. 628 traditional Chinese medicine prescriptions containing Danggui and Chuanxiong were extracted from the self-built prescription database and screened for the three groups of prescriptions, i.e., irregular menstruation, sores, and stroke. We processed and tested the dosage of Danggui and Chuanxiong and selected the optimal copula function, Gumbel copula function, from the Archimedes function family and elliptical copula function family to establish the data model. To establish the presence of a correlation between the dose of Danggui and Chuanxiong, a graph of the joint distribution function of rank correlation coefficients, Kendall's rank correlation coefficient and Spearman's rank correlation coefficient, was used. Our results suggest that the model using the Gumbel copula function better reflects the correlation between the dose of Danggui and Chuanxiong. For irregular menstruation, sores, and strokes, Kendall's rank correlation coefficients were 0.6724, 0.5930, and 0.7757, respectively, and Spearman's correlation coefficients were 0.8536, 0.7812, and 0.9285, respectively. In all three prescription groups, the dose of Danggui and Chuanxiong was positively correlated, implying that, as the dosage of one drug increases, the dosage of the other increases as well. From the perspective of data mining and mathematical statistics, the use of the copula function model to evaluate the correlation between the prescribed dosage of the two drugs was innovative and provided a new model for the scientific interpretation of the compatibility of traditional drugs. This might also serve to guide the clinical use of traditional Chinese medicine.