The increasing availability of electronic health data presents a major opportunity in
healthcare for both discovery and practical applications to improve healthcare. However, for …

Clinical data management systems typically provide caregiver teams with useful information,
derived from large, sometimes highly heterogeneous, data sources that are often changing …

These guidelines are intended for use by healthcare professionals who care for children and
adults with suspected or confirmed infectious diarrhea. They are not intended to replace …

Explainable classification is essential to high-impact settings where practitioners
requireevidence to support their decisions. However, state-of-the-art deep learning models …

Multivariate time series data are becoming increasingly common in numerous real world
applications, eg, power plant monitoring, health care, wearable devices, automobile, etc. As …

Leveraging historical behavioral data (eg, sales volume and email communication) for future
prediction is of fundamental importance for practical domains ranging from sales to temporal …

Rapid technology integration causes a high dimensional time series data accumulation in
multiple domains and applying the classical data mining tools and techniques becomes a …

Determining similarities (or distance) between multivariate time series sequences is a
fundamental problem in time series analysis. The complex temporal dependencies and …

Due to advances in mobile devices and sensors, there has been an increasing interest in
the analysis of multivariate time series. Identifying similar time series is a core subroutine in …

To develop and validate a prediction model for delayed cerebral ischemia (DCI) after
subarachnoid hemorrhage (SAH) using a temporal unsupervised feature engineering …