With rapid advancement in technology, the healthcare industry is producing and collecting data at a staggering speed. Vast amount of healthcare data has been collected through sources such as genomics, electronic health records, medical monitoring devices and health-related mobile phone apps, which result in a mixture of structured data such as patient demographics and medication list as well as semi-structured or unstructured data such as doctor notes and medical images. However, raw data is barely of direct interest to healthcare stakeholders unless potentially useful knowledge is extracted.
The advancement of data analytics facilitates the generation of data-driven models to improve the understanding of disease mechanisms, increase the efficiency in healthcare delivery, reduce overall cost to the healthcare systems and facilitate clinical decision support. The use of analytics in the ever-increasing quantity of healthcare data presents rich opportunities, but also a number of daunting challenges such as the vast amount of unstructured data, the concern of privacy and security issue, the lack of data standardization, the issues of data storage and transfers. These challenges have slowed the process of leveraging healthcare data, which leads to the deployment of analytics models in healthcare not convincingly demonstrated due to the rarity of their application.
To embrace the challenges and opportunities in designing and deploying intelligent healthcare systems, this special issue aims to encourage submissions of scientific findings from both academia and healthcare industry that present the fundamental theory, techniques, applications and practical experiences in the context of designing, implementing or evaluating analytics for healthcare intelligence.
The topics of this special issue include, but are not limited to:
Data Mining and Knowledge Discovery in Healthcare
Medical Expert Systems
Ontologies in Healthcare
Machine Learning in Healthcare
Clinical Decision Support Systems
Text Mining and Natural Language Processing in Medical Documents
Deep Learning Applications in Healthcare
Predictive Modelling for Personalized Treatment
Medical Recommender Systems
Intelligent Systems for Electronic Health Records
Computational Intelligence for Healthcare
Intelligent Medical Devices and Sensors
Visual Analytics for Healthcare
Modelling and Reasoning with Time in Medical Data and Systems