A process mining-based investigation of adverse events in care processes

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-world clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

Role-based process view derivation and composition

The process view concept deploys a partial and temporal representation to adjust the visible view of a business process according to various perception constraints of users. Process view technology is of practical use for privacy protection and authorization control in process-oriented business management. Owing to complex organizational structure, it is challenging for large companies to accurately specify the diverse perception of different users over business processes. Aiming to tackle this issue, this article presents a role-based process view model to incorporate role dependencies into process view derivation. Compared to existing process view approaches, ours particularly supports runtime updates to the process view perceivable to a user with specific view merging operations, thereby enabling the dynamic tracing of process perception. A series of rules and theorems are established to guarantee the structural consistency and validity of process view transformation. A hypothetical case is conducted to illustrate the feasibility of our approach, and a prototype is developed for the proof-of-concept purpose.

Compact features for birdcall retrieval from environmental acoustic recordings

Bioacoustic data can be used for monitoring animal species diversity. The deployment of acoustic sensors enables acoustic monitoring at large temporal and spatial scales. We describe a content-based birdcall retrieval algorithm for the exploration of large data bases of acoustic recordings. In the algorithm, an event-based searching scheme and compact features are developed. In detail, ridge events are detected from audio files using event detection on spectral ridges. Then event alignment is used to search through audio files to locate candidate instances. A similarity measure is then applied to dimension-reduced spectral ridge feature vectors. The event-based searching method processes a smaller list of instances for faster retrieval. The experimental results demonstrate that our features achieve better success rate than existing methods and the feature dimension is greatly reduced.

Comparative analysis of traffic state estimation ─ Cumulative counts-based and trajectory-based methods

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow. Since the MFD represents the area-wide network traffic performance, studies on perimeter control strategies and network-wide traffic state estimation utilising the MFD concept have been reported. Most previous works have utilised data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks due to queue spillovers at intersections. To overcome the limitation, recent literature reports the use of trajectory data obtained from probe vehicles. However, these studies have been conducted using simulated datasets; limited works have discussed the limitations of real datasets and their impact on the variable estimation. This study compares two methods for estimating traffic state variables of signalised arterial sections: a method based on cumulative vehicle counts (CUPRITE), and one based on vehicles’ trajectory from taxi Global Positioning System (GPS) log. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), due to which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for the network-wide traffic states.

An insight into the adoption of accountable-eHealth systems: n empirical research model based on the Australian context

This paper provides a first look at the acceptance of Accountable-eHealth (AeH) systems–a new genre of eHealth systems designed to manage information privacy concerns that hinder the proliferation of eHealth. The underlying concept of AeH systems is appropriate use of information through after-the-fact accountability for intentional misuse of information by healthcare professionals. An online questionnaire survey was utilised for data collection from three educational institutions in Queensland, Australia. A total of 23 hypotheses relating to 9 constructs were tested using a structural equation modelling technique. The moderation effects on the hypotheses were also tested based on six moderation factors to understand their role on the designed research model. A total of 334 valid responses were received. The cohort consisted of medical, nursing and other health related students studying at various levels in both undergraduate and postgraduate courses. Hypothesis testing provided sufficient data to accept 7 hypotheses. The empirical research model developed was capable of predicting 47.3% of healthcare professionals’ perceived intention to use AeH systems. All six moderation factors showed significant influence on the research model. A validation of this model with a wider survey cohort is recommended as a future study.

A novel concept for airport terminal design integrating flexibility

The current research proposed a conceptual design framework for airports to obtain flexible departure layouts based on passenger activity analysis obtained from Business Process Models (BPM). BPMs available for airport terminals were used as a design tool in the current research to uncover the relationships existing between spatial layout and corresponding passenger activities. An algorithm has been developed that demonstrates the applicability of the proposed design framework by obtaining relative spatial layouts based on passenger activity analysis. The generated relative spatial layout assists architects in achieving suitable alternative layouts to meet the changing needs of an airport terminal.

Nash equilibrium based semantic cache in mobile sensor grid database systems

Mobile applications are being increasingly deployed on a massive scale in various mobile sensor grid database systems. With limited resources from the mobile devices, how to process the huge number of queries from mobile users with distributed sensor grid databases becomes a critical problem for such mobile systems. While the fundamental semantic cache technique has been investigated for query optimization in sensor grid database systems, the problem is still difficult due to the fact that more realistic multi-dimensional constraints have not been considered in existing methods. To solve the problem, a new semantic cache scheme is presented in this paper for location-dependent data queries in distributed sensor grid database systems. It considers multi-dimensional constraints or factors in a unified cost model architecture, determines the parameters of the cost model in the scheme by using the concept of Nash equilibrium from game theory, and makes semantic cache decisions from the established cost model. The scenarios of three factors of semantic, time and locations are investigated as special cases, which improve existing methods. Experiments are conducted to demonstrate the semantic cache scheme presented in this paper for distributed sensor grid database systems.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.