Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The evolution of scientific literature as metastable knowledge states

The problem of identifying common concepts in the sciences and deciding when new ideas have emerged is an open one. Metascience researchers have sought to formalize principles underlying stages in the life cycle of scientific research, understand how knowledge is transferred between scientists and stakeholders, and explain how new ideas are generated and take hold. Here, we model the state of scientific knowledge immediately preceding new directions of research as a metastable state and the creation of new concepts as combinatorial innovation. Through a novel approach combining natural language clustering and citation graph analysis, we predict the evolution of ideas over time and thus connect a single scientific article to past and future concepts in a way that goes beyond traditional citation and reference connections.

Medical image processing and COVID-19: A literature review and bibliometric analysis

COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.

An Investigation into Art Therapy Aided Health and Well-Being Research: A 75-Year Bibliometric Analysis

Considering the physical, and psychological impacts and challenges brought about the coronavirus disease 2019 (COVID-19), art therapy (AT) provides opportunities to promote human health and well-being. There are few systematic analysis studies in the fields of AT, which can provide content and direction for the potential value and impact of AT. Therefore, this paper aims to critically analyze the published work in the field of AT from the perspective of promoting health and well-being, and provides insights into current research status, hotspots, limitations, and future development trends of AT. This paper adopts a mixed method of quantitative and qualitative analysis including bibliometric analysis and keyword co-occurrence analysis. The results indicate that: (1) the current studies on AT are mostly related to research and therapeutic methods, types of AT, research populations and diseases, and evaluation of therapeutic effect of AT. The research method of AT mainly adopts qualitative research, among which creative arts therapy and group AT are common types of AT, and its main research populations are children, veterans, and adolescents. AT-aided diseases are trauma, depression, psychosis, dementia, and cancer. In addition, the therapeutic methods are mainly related to psychotherapy, drama, music, and dance/movement. Further, computer systems are an important evaluation tool in the research of AT; (2) the future development trend of AT-aided health and well-being based on research hotspots, could be focused on children, schizophrenia, well-being, mental health, palliative care, veterans, and the elderly within the context of addressing COVID-19 challenges; and (3) future AT-aided health and well-being could pay more attention to innovate and integrate the therapeutic methods of behavior, movement, and technology, such as virtual reality and remote supervision.

Blockchain and Building Information Management (BIM) for Sustainable Building Development within the Context of Smart Cities

‘Smart cities’ are a new type of city where stakeholders are jointly responsible for urban management. City Information Management (CIM) is an output tool for smart city planning and management, which assists in achieving the sustainable development of urban infrastructure, and promotes smart cities to achieve the goals of stable global economic development, sustainable environmental development, and improvement of people’s quality of life. Existing research has so far established that blockchain and BIM have great potential to enhance construction project performance. However, there is little research on how blockchain and BIM can support sustainable building design and construction. Therefore, the aim of this paper is to explore the potential impact of the integration of blockchain and BIM in a smart city environment on making buildings more sustainable within the context of CIM/Smart Cities. The paper explores the relationships between blockchain, BIM and sustainable building across the life cycle stage of a construction project. This paper queries the Web of Science (WoS) database with keywords to obtain relevant publication, and then uses the VOSviewer to visually analyze the relationships between blockchain, BIM, and sustainable building within the context of smart cities and CIM, which is conducted in bibliometric analysis followed by micro scheme analysis. The results demonstrate the value of this method in gauging the importance of these three topics, highlighting their interrelationships and identifying trends, giving researchers an objective research direction. Those aspects reported in the paper constitute an original contribution.

Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017

BACKGROUND AND OBJECTIVE

Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970-2017).

METHODS

The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords.

RESULTS

CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB.

CONCLUSIONS

It can be concluded that CMPB is a world-renowned publication outlet for biomedical researchers which has been growing in a number of publications since 1970. The analysis also conclude that the journal is very international with publications from all over the world although today European countries are the most productive ones.

Deep learning for healthcare applications based on physiological signals: A review

BACKGROUND AND OBJECTIVE

We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017.

METHODS

An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review.

RESULTS

During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input.

CONCLUSIONS

This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.