ORCID Profile
0000-0002-1739-4398
Current Organisation
Taipei Medical University
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Publisher: MDPI AG
Date: 09-04-2021
DOI: 10.3390/HEALTHCARE9040441
Abstract: The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.
Publisher: Elsevier BV
Date: 10-2017
DOI: 10.1016/J.JBI.2017.08.008
Abstract: The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16 95%CI, 0.95-1.42 connectivity score [CS], 0.3016), zolpidem (OR, 1.11 95%CI, 0.95-1.30 CS, 0.2738), but not for lorazepam (OR, 1.04 95%CI, 0.89-1.23 CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.
Publisher: Oxford University Press (OUP)
Date: 11-05-2022
DOI: 10.1093/JAMIA/OCU019
Abstract: Objectives To objectively characterize phenome-wide associations observed in the entire Taiwanese population and represent them in a meaningful, interpretable way. Study Design In this population-based observational study, we analyzed 782 million outpatient visits and 15 394 unique phenotypes that were observed in the entire Taiwanese population of over 22 million in iduals. Our data was obtained from Taiwan’s National Health Insurance Research Database. Results We stratified the population into 20 gender-age groups and generated 28.8 million and 31.8 million pairwise odds ratios from male and female subpopulations, respectively. These associations can be accessed online at associations.phr.tmu.edu.tw. To demonstrate the database and validate the association estimates obtained, we used correlation analysis to analyze 100 phenotypes that were observed to have the strongest positive association estimates with respect to essential hypertension. The results indicated that association patterns tended to have a strong positive correlation between adjacent age groups, while correlation estimates tended to decline as groups became more distant in age, and they erged when assessed across gender groups. Conclusions The correlation analysis of pairwise disease association patterns across different age and gender groups led to outcomes that were broadly predicted before the analysis, thus confirming the validity of the information contained in the presented database. More erse in idual disease-specific analyses would lead to a better understanding of phenome-wide associations and empower physicians to provide personalized care in terms of predicting, preventing, or initiating an early management of concomitant diseases.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2019
No related grants have been discovered for Wen-Shan Jian.