According to the Global Health Observatory, stroke ranks second worldwide in causing dementia, right after Alzheimer’s disease. The mortality rate linked to dementia resulting from stroke is high because symptoms are often recognized late, and stroke can be misinterpreted as other brain disorders. Early detection and diagnosis of stroke is crucial. Therefore, increasing awareness of stroke symptoms and implementing preventive measures becomes imperative. Prompt intervention by healthcare professionals can improve outcomes and reduce long-term complications of stroke. The research introduces an innovative approach for early stroke prediction using a fuzzy scoring-based Decision Support System. This approach encompasses three main modules: Mind map-based Data Modeling, Fuzzy scoring computing, and Machine Learning (ML)-Based Decision System. By incorporating fuzzy logic, the approach extracts valuable knowledge from imprecise and uncertain data. Combining the fuzzy stroke risk model with a ML-based decision support system aims to enhance stroke prediction accuracy and improve preventive measures and patient outcomes. The approach’s effectiveness was validated using real clinical data and tested with various ML classifiers, including K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The results showed a strong correlation between stroke cases and computed risk-scoring values. In comparison to predictions without fuzzy scoring and other related works, the stroke risk prediction using the proposed approach demonstrated higher accuracy, making it a promising method for early stroke detection and prevention.

Comparative studies between software multiplatform development frameworks lack a proper approach that can be replicated in future performance assessments. Moreover, there is still a deficit in performance comparison tools. Also, performance comparisons realized between mobile applications developed under these multiplatform frameworks should be done with applications running in Release Mode, which ends up not happening in most studies. The objective of this paper is thus to create a whole comparative process as correct and stable as possible, so that we can use it to safely assess performance of mobile applications developed with these frameworks. As a case study, we compare the well-known Flutter and React Native frameworks, and present the obtained results under the proposed approach. With this work, developers can not only assess both these particular frameworks, but also use the approach for further comparisons.

Adolescents, reportedly, easily adopt a sedentary lifestyle and unhealthy behaviors. Furthermore, the use of smartphones, especially in this age group, is made on a daily basis. In this context, the TeenPower Project includes an mHealth platform integrated in an intervention programme to engage teenagers to embrace more healthier lifestyle behaviors. This paper describes the technical aspects of the development and implementation of the software applications that compose the TeenPower Platform. The platform consists of two applications: a mobile app (Android) to be used by adolescents, mainly for self-monitoring and educational purposes; and the backoffice (web) application to be used by teachers and health professionals. Both applications are integrated and work through a web service (REST-based) API, which allows them to exchange data seamlessly, keep all related contents updated and enhance communication between adolescents and health professionals.

During the last decades, flexibility has gained a strong presence, in a variety of disciplines, mainly in the BPM field. The real challenge for BPM consists in providing modeling paradigms and BPMSs with adequate information and features to deal with the often conflicting requirements of flexibility. In this setting, we focus on providing a guidance approach for enhancing business process flexibility. Our purpose is therefore to perceive which modeling paradigm(s) and/or business process management system(s) (BPMS(s)) are the most adequate to the specific organization needs in terms of flexibility. This approach was implemented in a plug-in named BPFlexGuide. To evaluate this approach, we have studied the emergency care (EC) process. Users interested in the EC process were guided to use the AristaFlow BPM suite BPMS. The results of this study would help designers to choose the best paradigms and BPMS that best fit their needs on flexibility.

Business Process Management (BPM) is being increasingly adopted to streamline healthcare service delivery and management processes. Emergency departments (ED) are very complex. To operate effectively and create value, EDs must be flexible and have the ability to rapidly adapt to the highly variable needs of patients. In this paper, we provide models for a real-world scenario emergency care (EC) process, and elicit its flexibility needs using interviews with the EC process participants. We then use a guidance tool to help with the choice of the most suitable Business Process Management System (BPMS), regarding the flexibility needs elicited. We validate this choice by modelling the new flexibility enhanced EC process with one of the advised BPMSs, and analyse its support regarding the elicited flexibility needs.

Organizations that include several organizational units with similar business processes often suffer, with time, from the proliferation of processes variant models that significantly deviate from the original (to be followed) one. Take, for instance, student enrolment processes on distinct faculties of the same university, or healthcare processes of a National Health Service across distinct health centers. This can lead to poor global (process) management, since measuring and improving processes can be difficult with too many variants of the same business process. Related works analyse the generalisation and flexibility aspects of process models and related variants, but do not deal with the overall process model lifecycle, especially for this kind of organizations. This paper introduces a novel approach for the Governance and continuous improvement of process models for this kind of organizations. The approach is based on the general Business Process Management (BPM) cycle for process models, proposing concrete techniques for the phases of evaluation, classification and analysis between real and concrete variant models from each organizational unit. It includes the use of similarity metrics and flexibility in business processes, and the main output is a continuously improved template process model. This template foresees a common process part (best practice-based) including process elements collected from the process model variants verified in organizational units, and a flexible part, referring to possible (controlled) deviations that can be tolerated by the organization's headquarters. This approach enhances overall business process management and associated resources by enforcing uniform (good) behavior across similar organizational units. We present the results of our approach applied to a real-world case study of home-healthcare related business process models.

In this paper, we present a two-step approach for modelling controlled flexibility in software processes: (1) senior process engineers express which, where and how changes can be applied onto process elements, (2) other process participants can easily identify which changes they are allowed to perform, and act accordingly. To support this, we propose a flexibility meta-model and modelling language, and a software process modelling tool called FlexEPFC. Finally, we compare FlexEPFC with three other prominent process-aware tools: SPADE, JIL/Juliette and Jazz.