Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

Smart city environments, when applied to healthcare, improve the quality of people\u27s lives, enabling, for instance, disease prediction and treatment monitoring. In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians\u27 daily work. Recommender systems are an alternative to automatically integrate the data generated in such environments with predictive models and recommend actions, content, or services. The data produced by smart devices are accurate and reliable for predictive and decision-making contexts. This study main purpose is to assist patients and doctors in the early detection of disease or prediction of postoperative worsening through constant monitoring. To achieve this objective, this study proposes an architecture for recommender systems applied to healthcare, which can prioritize emergency cases. The architecture brings an ensemble approach for prediction, which adopts multiple Machine Learning algorithms. The methodology used to carry out the study followed three steps. First, a systematic literature mapping, second, the construction and development of the architecture, and third, the evaluation through two case studies. The results demonstrated the feasibility of the proposal. The predictions are promising and adherent to the application context for accurate datasets with a low amount of noises or missing values

Learning HCI Across Institutions, Disciplines and Countries: A Field Study of Cognitive Styles in Analytical and Creative Tasks

Human-computer interaction (HCI) is increasingly becoming a subject taught in universities around the world. However, little is known of the interactions of the HCI curriculum with students in different types of institutions and disciplines internationally. In order to explore these interactions, we studied the performance of HCI students in design, technology and business faculties in universities in UK, India, Namibia, Mexico and China who participated in a common set of design and evaluation tasks. We obtained participants’ cognitive style profiles based on Allinson and Hayes scale in order to gain further insights into their learning styles and explore any relation between these and performance. We found participants’ cognitive style preferences to be predominantly in the adaptive range, i.e. with combined analytical and intuitive traits, compared to normative data for software engineering, psychology and design professionals. We further identified significant relations between students’ cognitive styles and performance in analytical and creative tasks of a HCI professional individual. We discuss the findings in the context of the distinct backgrounds of the students and universities that participated in this study and the value of research that explores and promotes diversity in HCI education