Dear Colleagues,

Health is our lifelong goal. The world has experienced severe medical issues, such as an aging population, medical staff shortage and poor quality of life. Thanks to those who have devoted efforts to contributing to the healthcare industries, the advancement of big data, Internet of Things (IoT) and artificial intelligence (AI) have brought a glimmer of success in data science for healthcare applications.                           

In today’s era of data explosion, there are growing and unlimited health data that need to be collected, stored, transferred, investigated, analyzed and utilized. Data science plays a lead role in healthcare intelligence applications. It aims at enabling researchers and enterprises to formulate processing and analysis methods to extract latent information from multiple data resources and to leverage a broad range of data handling and computational platforms.

Medical workers may argue that there may be a conflict of interest between data science and their role, but this is not true. On the contrary, the two are not only able to co-exist but even complement each other. First, the current workload of medical workers (ratio of workers to patients) is heavy but can be reduced through the use of data science. Second, automatic systems focus on routine works so that medical workers can devote more time to professional consultation and surgery activities. Third, the increase in quality of medical services will lead to higher acceptance and satisfaction by the public. Thus, medical workers will achieve a higher social status and better job satisfaction. It is thus evident that the use of data science in the medical field is a win-win situation.

This Special Issue is intended to report high-quality research on recent advances towards data science for healthcare intelligence, more specifically state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of those convergence technologies for providing insights into healthcare intelligence. Topics include but are not limited to:

• Data warehouse and data mining for healthcare intelligence;
• Emerging data science techniques for healthcare intelligence;
• Descriptive, diagnostic, predictive and prescriptive analytics for healthcare applications;
• Big data, cloud and the IoT platform and architecture;
• Data quality, privacy, policy and security;
• Shallow learning and deep learning for healthcare intelligence;
• Political economy in healthcare;
• Machine learning algorithms for disease diagnosis;
• Semantic web technologies in healthcare;
• Natural language processing in healthcare;
• Virtual reality and augmented reality in healthcare;
• Clinical informatics, bioinformatics, imagining informatics, consumer health informatics, research informatics and public health informatics.

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