TY - GEN
T1 - A web-based knowledge hub for exploration of multiple research article collections
AU - Zhang, Wei Emma
AU - Liu, Miao
AU - Pallath, Alan
AU - Tamilventhan, Gokul
PY - 2021
Y1 - 2021
N2 - Medical decision-making is guided by the results of rich medical research and clinical trials. Doctors, practitioners and researchers urgently need to get updated by the most recent research and clinical outputs to make correct decisions, especially when a new virus such as Covid-19 is causing a global epidemic. However, medical literature for a certain topic could be from different aspects thus archived in different literature databases, resulting in the searching for all related articles become a laborious and time-consuming task. It becomes worse when there is a rapid growth of the number of published literature for the given topic. In this work, we build an online knowledge hub (http://covid19knowledgehub.herokuapp.com/) particularly for Covid-19 related research articles per the requirement from researchers in a hospital. The system is built on top of nine medical research article databases, which covers a wide range of medical aspects. It allows users to easily retrieve and explore the articles from multiple literature databases at one-stop. The system also provides the statistics of article distributions to offer an overview of the status of research under this topic. This real-demand driven system is deployed in a research team of Renmin Hospital, Wuhan University, and largely reduces their time for searching the latest articles. Although this project focuses on Covid-19 related research articles, the approach at the back could be applied to any topic in any domain.
AB - Medical decision-making is guided by the results of rich medical research and clinical trials. Doctors, practitioners and researchers urgently need to get updated by the most recent research and clinical outputs to make correct decisions, especially when a new virus such as Covid-19 is causing a global epidemic. However, medical literature for a certain topic could be from different aspects thus archived in different literature databases, resulting in the searching for all related articles become a laborious and time-consuming task. It becomes worse when there is a rapid growth of the number of published literature for the given topic. In this work, we build an online knowledge hub (http://covid19knowledgehub.herokuapp.com/) particularly for Covid-19 related research articles per the requirement from researchers in a hospital. The system is built on top of nine medical research article databases, which covers a wide range of medical aspects. It allows users to easily retrieve and explore the articles from multiple literature databases at one-stop. The system also provides the statistics of article distributions to offer an overview of the status of research under this topic. This real-demand driven system is deployed in a research team of Renmin Hospital, Wuhan University, and largely reduces their time for searching the latest articles. Although this project focuses on Covid-19 related research articles, the approach at the back could be applied to any topic in any domain.
KW - literature collection
KW - web-based retrieval
KW - document analysis
UR - http://www.scopus.com/inward/record.url?scp=85111649888&partnerID=8YFLogxK
U2 - 10.1145/3404835.3462780
DO - 10.1145/3404835.3462780
M3 - Conference proceeding contribution
AN - SCOPUS:85111649888
T3 - SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 2556
EP - 2559
BT - SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc
CY - New York, NY
T2 - 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
Y2 - 11 July 2021 through 15 July 2021
ER -