The ontology framework and challenges of smart healthcare system transformation using natural language processing and latent Dirichlet allocation. - Zhao, Shuyan; Zhong, Hua; Ge, Beibei; Zhao, Xiaojing (Health Informatics Journal)
Objectives: This article aims to develop the ontology framework of smart healthcare system and identify the challenges to construct the smart healthcare system. The ontology framework provides both academics and practitioners a reference to understand and transform the healthcare system. Methods: The publications in the area of the smart healthcare system were extracted from WOS core collection database. Latent Dirichlet Allocation (LDA) was employed to find subjects of publications. Natural language processing (NLP) was used to extract entities from topics explored based on LDA. The developed ontology framework of the smart healthcare system was then presented in OWL format using Protégé software. The challenges in transforming towards the smart healthcare system were identified based on the developed ontology framework. Results: Fourteen challenges are identified through the ontology framework developed by NLP and LDA, including poor system interoperability, data security and data sharing, low adoption of data standards and data scalability, etc. These challenges provide a reference for future healthcare workers to deal with possible risks and difficulties. Conclusions: The ontology framework developed by NLP and LDA provides a unified description and structured knowledge in smart healthcare system, and provides valuable working methods and management basis for scholars and medical workers.