In this paper, a new algorithm denoted as FilterK is proposed for improving the purity of k-means derived physical activity clusters by reducing outlier influence. We applied it to physical activity data obtained with body-worn accelerometers and clustered using k-means. We compared its performance with three existing outlier detection methods: Local Outlier Factor, Isolation Forests and KNN using

INTRODUCTION Health care has been deeply revolutionized by the new wave of information and communication technology (ICT) development, particularly related to the electronic/personal health record (PHR). The present paper describes the original design and implementation approach followed in the Trentino Province (Italy) to promote an Integrated Care Model for patients' empowerment and data repository

Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic

A byproduct of the transition to electronic health records (EHRs) is the associated observational data that capture EHR users' granular interactions with the medical record. Often referred to as audit log data or event log data, these datasets capture and timestamp user activity while they are logged in to the EHR. These data - alone and in combination with other datasets - offer a new source of insights