Multimodal data-driven approach has emerged as an important driving force for smart healthcare systems with applications ranging from disease analysis to triage, diagnosis and treatment. Smart healthcare system necessitates new demands for data management and decision-making, which has inspired the rapid development of medical services using artificial intelligence and new transformations in the healthcare industry. In this paper, we provide a comprehensive survey of existing techniques which include not only state-of-the-art methods but also the most recent trends in the field. In particular, this review focuses on the types of decision-making processes used in smart healthcare systems. Firstly, approaches that utilize multimodal association mining with fine-grained data semantics in smart healthcare systems are introduced. We review the smart healthcare-oriented semantic perception, semantic alignment, entity association mining, and discuss the pros and cons of these approaches. Secondly, we discuss approaches for multimodal data fusion and cross-border association that have been employed in developing smart healthcare systems. Finally, we focus specifically on the use of the panoramic decision framework, interactive decision making, and intelligent decision support systems. We introduce how smart healthcare systems can be applied to and benefit a wide variety of fields, including knowledge discovery and privacy protection.

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. Compared with grid environment, data transfer is a big overhead for cloud workflows due to the market-oriented business model in the cloud environments. In this paper, a Revised Discrete Particle Swarm Optimization (RDPSO) is proposed to schedule applications among cloud services that takes both data transmission cost and computation cost into account. Experiment is conducted with a set of workflow applications by varying their data communication costs and computation costs according to a cloud price model. Comparison is made on make span and cost optimization ratio and the cost savings with RDPSO, the standard PSO and BRS (Best Resource Selection) algorithm. Experimental results show that the proposed RDPSO algorithm can achieve much more cost savings and better performance on make span and cost optimization.

The concept of cloud computing continues to spread widely, as it has been accepted recently. Cloud computing has many unique advantages which can be utilized to facilitate workflow execution. Instance-intensive cost-constrained cloud workflows are workflows with a large number of workflow instances (i.e. instance intensive) bounded by a certain budget for execution (i.e. cost constrained) on a cloud computing platform (i.e. cloud workflows). However, there are, so far, no dedicated scheduling algorithms for instance-intensive cost-constrained cloud workflows. This paper presents a novel compromised-time-cost scheduling algorithm which considers the characteristics of cloud computing to accommodate instance-intensive cost-constrained workflows by compromising execution time and cost with user input enabled on the fly. The simulation performed demonstrates that the algorithm can cut down the mean execution cost by over 15% whilst meeting the user-designated deadline or shorten the mean execution time by over 20% within the user-designated execution cost.