The use of machine learning models to predict clinical outcomes from (longitudinal)
electronic health record (EHR) data is becoming increasingly popular due to advances in …

Time-series analysis plays a pivotal role across a range of critical applications, from finance
to healthcare, which involves various tasks, such as forecasting and classification. To handle …

Wearable sensors have become ubiquitous thanks to a variety of health tracking features.
The resulting continuous and longitudinal measurements from everyday life generate large …

Deep neural networks, including the Transformer architecture, have achieved remarkable
performance in various time series tasks. However, their effectiveness in handling clinical …

Heart failure is a debilitating condition that affects millions of people worldwide and has a
significant impact on their quality of life and mortality rates. An objective assessment of …

The proliferation of digital interactions across diverse domains, such as healthcare, e-
commerce, gaming, and finance, has resulted in the generation of vast volumes of event …

Neural time-series analysis has traditionally focused on modeling data in the time domain,
often with some approaches incorporating equivalent Fourier domain representations as …

Recent industrial applications in risk prediction still heavily rely on extensively manually-
tuned, statistical learning methods. Real-world financial data, characterized by its high …

Learning a decent representation from unlabeled time series is a challenging task,
especially when the time series data is derived from diverse channels at different sampling …

We propose a deep generative approach using latent temporal processes for modeling and
holistically analyzing complex disease trajectories, with a particular focus on Systemic …