Training skillful and competent surgeons is critical to ensure high quality of care and to
minimize disparities in access to effective care. Traditional models to train surgeons are …

Electronic health records contain large amounts of longitudinal data that are valuable for
biomedical informatics research. The application of machine learning is a promising …

Abstract Background Adverse drug events (ADEs) as well as other preventable adverse
events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in …

The next generation of human activity recognition applications in ubiquitous computing
scenarios focuses on assessing the quality of activities, which goes beyond mere …

Satellite remote sensing has become an important means of forest fire monitoring because it
has the advantages of wide coverage, few ground constraints and high dynamics. When …

The enhanced multi-objective symbolic discretization for time series (eMODiTS) method
employs a flexible discretization scheme using different value cuts for each non-equal time …

This paper presents the design and a first evaluation of a new monitoring system based on
contactless sensors to estimate sleep quality. This sensor produces thermal signals which …

Detecting anomalies in time series in real time can be challenging, in particular when
anomalies can manifest themselves at different time scales and need to be detected with …

Time series discretization is a technique commonly used to tackle time series classification
problems. This manuscript presents an enhanced multi-objective approach for the symbolic …

Time series data are widely found in finance, health, environmental, social, mobile and other
fields. A large amount of time series data has been produced due to the general use of …