Uncovering Hidden Topics in Hong Kong Clinical Research Through Hospital Authority Convention Publications
Uncovering clinical research trends allows us to understand the direction of healthcare services and is essential for longer-term healthcare planning. The Hospital Authority Convention is a mainstream annual healthcare conference that gathers up-to- date Hong Kong medical research. We propose to use state-of- the-art medical document mining and topic modelling methods to uncover latent themes and structures in the publications. We collected 742 articles from HA Convention from the year 2013 to 2016 and selected 56 publications from the category of Clinical Safety and Quality Service for further analysis. Applying natural language processing and Latent Dirichlet Allocation (LDA) methods, we identified 7 potential topics, namely: surgical operation, hospital discharge, medical error, nursing procedure, service performance assessment, patient and staff engagement, and admission algorithm and standardisation. This exploratory study demonstrates that key themes exist in the annual HA Convention and we observe potential changes in healthcare services focus over the years in the selected category.
Data Mining Unsupervised Machine Learning,Medical Informatics
Zoie Shui-Yee Wong Wai-Man Chan Eliza Lai-Yi Wong Patsy Yuen-Kwan Chau Kwok-Leung Tsui Hong Fung
Graduate School of Public Health,St. Lukes International University,Tokyo,Japan;School of Public He Department of Systems Engineering and Engineering Management,City University of Hong Kong,Hong Kong, JC School of Public Health and Primary Care,The Chinese University of Hong Kong,Hong Kong,China
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
624-628
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）