In this paper, we present a new deep learning architecture for addressing the problem of
supervised learning with sparse and irregularly sampled multivariate time series. The …

Objectives: To review how health informatics systems based on machine learning methods
have impacted the clinical management of patients, by affecting clinical practice. Methods …

We present the first fully variational Bayesian inference scheme for continuous Gaussian-
process-modulated Poisson processes. Such point processes are used in a variety of …

Introducing inequality constraints in Gaussian processes can lead to more realistic
uncertainties in learning a great variety of real-world problems. We consider the finite …

It is a surprising fact that electronic medical records are failing at one of their primary
purposes, that of tracking the set of medications that the patient is actively taking. Studies …

Point process data are commonly observed in fields like healthcare and social science.
Designing predictive models for such event streams is an under-explored problem, due to …

Temporal point process (TPP) models combined with recurrent neural networks provide a
powerful framework for modeling continuous-time event data. While such models are …

Patients in hospitals, particularly in critical care, are susceptible to many complications
affecting morbidity and mortality. Digitized clinical data in electronic medical records can be …

Real-world event sequences consist of complex mixtures of different types of events
occurring in time. An event may depend on past events of the same type, as well as, the …

Animals in groups often exchange calls, in patterns whose temporal structure may be
influenced by contextual factors such as physical location and the social network structure of …