The Experts below are selected from a list of 271977 Experts worldwide ranked by ideXlab platform

Bach Xuan Tran - One of the best experts on this subject based on the ideXlab platform.

  • modeling Research topics for artificial intelligence applications in medicine latent dirichlet allocation application study
    Journal of Medical Internet Research, 2019
    Co-Authors: Bach Xuan Tran, Carl A Latkin, Son Nghiem, Oz Sahin, Hai Quang Pham, Wilson W S Tam
    Abstract:

    Background: Artificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and Research Landscape of AI in this field. Objective: This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary Research topics on the theory and practice of AI in medicine from 1977 to 2018. Methods: We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the Research Landscape. Results: The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. Conclusions: The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

  • the current Research Landscape of the application of artificial intelligence in managing cerebrovascular and heart diseases a bibliometric and content analysis
    International Journal of Environmental Research and Public Health, 2019
    Co-Authors: Bach Xuan Tran, Carl A Latkin, Huong Lan Thi Nguyen, Son Nghiem, Ming Xuan Tan, Zhi Kai Lim
    Abstract:

    The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest of the Research and medical community. This study aims to provide a comprehensive picture of global trends and developments of AI applications relating to stroke and heart diseases, identifying Research gaps and suggesting future directions for Research and policy-making. A novel analysis approach that combined bibliometrics analysis with a more complex analysis of abstract content using exploratory factor analysis and Latent Dirichlet allocation, which uncovered emerging Research domains and topics, was adopted. Data were extracted from the Web of Science database. Results showed topics with the most compelling growth to be AI for big data analysis, robotic prosthesis, robotics-assisted stroke rehabilitation, and minimally invasive surgery. The study also found an emerging Landscape of Research that was centered on population-specific and early detection of stroke and heart disease. Application of AI in health behavior tracking and improvement as well as the use of robotics in medical diagnostics and prognostication have also been found to attract significant Research attention. In light of these findings, it is suggested that the currently under-Researched issues of data management, AI model reliability, as well as validation of its clinical utility, need to be further explored in future Research and policy decisions to maximize the benefits of AI applications in stroke and heart diseases.

  • the current Research Landscape on the artificial intelligence application in the management of depressive disorders a bibliometric analysis
    International Journal of Environmental Research and Public Health, 2019
    Co-Authors: Bach Xuan Tran, Roger S Mcintyre, Carl A Latkin, Hai Thanh Phan, Huong Lan Thi Nguyen, Kenneth K Gwee
    Abstract:

    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.

Paramvir Singh - One of the best experts on this subject based on the ideXlab platform.

  • How does Object-Oriented Code Refactoring Influence Software Quality? Research Landscape and Challenges
    Journal of Systems and Software, 2019
    Co-Authors: Satnam Kaur, Paramvir Singh
    Abstract:

    Abstract Context Software refactoring aims to improve software quality and developer productivity. Numerous empirical studies investigating the impact of refactoring activities on software quality have been conducted over the last two decades. Objective This study aims to perform a comprehensive systematic mapping study of existing empirical studies on evaluation of the effect of object-oriented code refactoring activities on software quality attributes. Method We followed a multi-stage scrutinizing process to select 142 primary studies published till December 2017. The selected primary studies were further classified based on several aspects to answer the Research questions defined for this work. In addition, we applied vote-counting approach to combine the empirical results and their analysis reported in primary studies. Results The findings indicate that studies conducted in academic settings found more positive impact of refactoring on software quality than studies performed in industries. In general, refactoring activities caused all quality attributes to improve or degrade except for cohesion, complexity, inheritance, fault-proneness and power consumption attributes. Furthermore, individual refactoring activities have variable effects on most quality attributes explored in primary studies, indicating that refactoring does not always improve all quality attributes. Conclusions This study points out several open issues which require further investigation, e.g., lack of industrial validation, lesser coverage of refactoring activities, limited tool support, etc.

Luca Mazzarella - One of the best experts on this subject based on the ideXlab platform.

  • semantic and geographical analysis of covid 19 trials reveals a fragmented clinical Research Landscape likely to impair informativeness
    Frontiers in Medicine, 2020
    Co-Authors: Giulia Tini, Bruno Achutti Duso, Federica Bellerba, Federica Corso, Sara Gandini, Saverio Minucci, Pier Giuseppe Pelicci, Luca Mazzarella
    Abstract:

    Background: The unprecedented impact of the COVID-19 pandemic on modern society has ignited a "gold rush" for effective treatment and diagnostic strategies, with a significant diversion of economic, scientific, and human resources toward dedicated clinical Research. We aimed to describe trends in this rapidly changing Landscape to inform adequate resource allocation. Methods: We developed an online repository (COVID Trial Monitor) to analyze in real time the growth rate, geographical distribution, and characteristics of COVID-19 related trials. We defined structured semantic ontologies with controlled vocabularies to categorize trial interventions, study endpoints, and study designs. Analyses are publicly available at https://bioinfo.ieo.it/shiny/app/CovidCT. Results: We observe a clear prevalence of monocentric trials with highly heterogeneous endpoints and a significant disconnect between geographic distribution and disease prevalence, implying that most countries would need to recruit unrealistic percentages of their total prevalent cases to fulfill enrolment. Conclusions: This geographically and methodologically incoherent growth casts doubts on the actual feasibility of locally reaching target sample sizes and the probability of most of these trials providing reliable and transferable results. We call for the harmonization of clinical trial design criteria for COVID-19 and the increased use of larger master protocols incorporating elements of adaptive designs. COVID Trial Monitor identifies critical issues in current COVID-19-related clinical Research and represents a useful resource with which Researchers and policymakers can improve the quality and efficiency of related trials.

  • semantic and geographical analysis of covid 19 trials reveals a fragmented clinical Research Landscape likely to impair informativeness
    medRxiv, 2020
    Co-Authors: Giulia Tini, Bruno Achutti Duso, Federica Bellerba, Federica Corso, Sara Gandini, Saverio Minucci, Pier Giuseppe Pelicci, Luca Mazzarella
    Abstract:

    Abstract Background The unprecedented impact of the Covid-19 pandemics on modern society has ignited a “gold rush” for effective treatment and diagnostic strategies, with a significant diversion of economical, scientific and human resources towards dedicated clinical Research. We aimed to describe trends in this rapidly changing Landscape to inform adequate resource allocation. Methods We developed informatic tools (Covid Trial Monitor) to analyze in real time growth rate, geographical distribution and characteristics of Covid-19 related trials. We defined structured semantic ontologies with controlled vocabularies to categorize trial interventions, study endpoints and study designs. Data and analyses are publicly available at https://bioinfo.ieo.it/shiny/app/CovidCT Results We observe a clear prevalence of monocentric trials with highly heterogeneous endpoints and a significant disconnect between geographic distribution and disease prevalence, implying that most countries would need to recruit unrealistic percentages of their total prevalent cases to fulfill enrolment. Conclusions This geographically and methodologically incoherent growth sheds doubts on the actual feasibility of locally reaching target sample sizes and the probability of most of these trials providing reliable and transferable result. We call for the harmonization of clinical trial design criteria for Covid19 and the increased use of larger master protocols incorporating elements of adaptive designs. Covid Trial Monitor identifies critical issues in current Covid19-related clinical Research and represents a useful resource for Researchers and policymakers to improve the quality and efficiency of related trials

Pier Giuseppe Pelicci - One of the best experts on this subject based on the ideXlab platform.

  • semantic and geographical analysis of covid 19 trials reveals a fragmented clinical Research Landscape likely to impair informativeness
    Frontiers in Medicine, 2020
    Co-Authors: Giulia Tini, Bruno Achutti Duso, Federica Bellerba, Federica Corso, Sara Gandini, Saverio Minucci, Pier Giuseppe Pelicci, Luca Mazzarella
    Abstract:

    Background: The unprecedented impact of the COVID-19 pandemic on modern society has ignited a "gold rush" for effective treatment and diagnostic strategies, with a significant diversion of economic, scientific, and human resources toward dedicated clinical Research. We aimed to describe trends in this rapidly changing Landscape to inform adequate resource allocation. Methods: We developed an online repository (COVID Trial Monitor) to analyze in real time the growth rate, geographical distribution, and characteristics of COVID-19 related trials. We defined structured semantic ontologies with controlled vocabularies to categorize trial interventions, study endpoints, and study designs. Analyses are publicly available at https://bioinfo.ieo.it/shiny/app/CovidCT. Results: We observe a clear prevalence of monocentric trials with highly heterogeneous endpoints and a significant disconnect between geographic distribution and disease prevalence, implying that most countries would need to recruit unrealistic percentages of their total prevalent cases to fulfill enrolment. Conclusions: This geographically and methodologically incoherent growth casts doubts on the actual feasibility of locally reaching target sample sizes and the probability of most of these trials providing reliable and transferable results. We call for the harmonization of clinical trial design criteria for COVID-19 and the increased use of larger master protocols incorporating elements of adaptive designs. COVID Trial Monitor identifies critical issues in current COVID-19-related clinical Research and represents a useful resource with which Researchers and policymakers can improve the quality and efficiency of related trials.

  • semantic and geographical analysis of covid 19 trials reveals a fragmented clinical Research Landscape likely to impair informativeness
    medRxiv, 2020
    Co-Authors: Giulia Tini, Bruno Achutti Duso, Federica Bellerba, Federica Corso, Sara Gandini, Saverio Minucci, Pier Giuseppe Pelicci, Luca Mazzarella
    Abstract:

    Abstract Background The unprecedented impact of the Covid-19 pandemics on modern society has ignited a “gold rush” for effective treatment and diagnostic strategies, with a significant diversion of economical, scientific and human resources towards dedicated clinical Research. We aimed to describe trends in this rapidly changing Landscape to inform adequate resource allocation. Methods We developed informatic tools (Covid Trial Monitor) to analyze in real time growth rate, geographical distribution and characteristics of Covid-19 related trials. We defined structured semantic ontologies with controlled vocabularies to categorize trial interventions, study endpoints and study designs. Data and analyses are publicly available at https://bioinfo.ieo.it/shiny/app/CovidCT Results We observe a clear prevalence of monocentric trials with highly heterogeneous endpoints and a significant disconnect between geographic distribution and disease prevalence, implying that most countries would need to recruit unrealistic percentages of their total prevalent cases to fulfill enrolment. Conclusions This geographically and methodologically incoherent growth sheds doubts on the actual feasibility of locally reaching target sample sizes and the probability of most of these trials providing reliable and transferable result. We call for the harmonization of clinical trial design criteria for Covid19 and the increased use of larger master protocols incorporating elements of adaptive designs. Covid Trial Monitor identifies critical issues in current Covid19-related clinical Research and represents a useful resource for Researchers and policymakers to improve the quality and efficiency of related trials

Satnam Kaur - One of the best experts on this subject based on the ideXlab platform.

  • How does Object-Oriented Code Refactoring Influence Software Quality? Research Landscape and Challenges
    Journal of Systems and Software, 2019
    Co-Authors: Satnam Kaur, Paramvir Singh
    Abstract:

    Abstract Context Software refactoring aims to improve software quality and developer productivity. Numerous empirical studies investigating the impact of refactoring activities on software quality have been conducted over the last two decades. Objective This study aims to perform a comprehensive systematic mapping study of existing empirical studies on evaluation of the effect of object-oriented code refactoring activities on software quality attributes. Method We followed a multi-stage scrutinizing process to select 142 primary studies published till December 2017. The selected primary studies were further classified based on several aspects to answer the Research questions defined for this work. In addition, we applied vote-counting approach to combine the empirical results and their analysis reported in primary studies. Results The findings indicate that studies conducted in academic settings found more positive impact of refactoring on software quality than studies performed in industries. In general, refactoring activities caused all quality attributes to improve or degrade except for cohesion, complexity, inheritance, fault-proneness and power consumption attributes. Furthermore, individual refactoring activities have variable effects on most quality attributes explored in primary studies, indicating that refactoring does not always improve all quality attributes. Conclusions This study points out several open issues which require further investigation, e.g., lack of industrial validation, lesser coverage of refactoring activities, limited tool support, etc.