Modern predictive models require large amounts of data for training and evaluation,
absence of which may result in models that are specific to certain locations, populations in …

Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the
key steps towards improving patient survival. Learning trajectories from physiological …

Algorithm–based clinical decision support (CDS) systems associate patient-derived health
data with outcomes of interest, such as in-hospital mortality. However, the quality of such …

In recent years, the field of pattern mining has shifted to user-centered methods. In such a
context, it is necessary to have a tight coupling between the system and the user where …

A large volume of research has considered the creation of predictive models for clinical data;
however, much existing literature reports results using only a single source of data. In this …

Ensemble methods have been successfully applied in a wide range of scenarios, including
survival analysis. However, most ensemble models for survival analysis consist of models …

The microbiota has proved to be one of the critical factors for many diseases, and
researchers have been using microbiome data for disease prediction. However, models …

The recurrence of Ischemic cerebrovascular events (ICE) often results in a high rate of
mortality and disability. However, due to the lack of labeled follow-up data in hospitals …

Asthma affects 9% of Americans and incurs US $56 billion in cost, 439,000 hospitalizations,
and 1.8 million emergency room visits annually. A small fraction of asthma patients with high …

Accurate assessment of the severity of a patient's condition plays a fundamental role in
acute hospital care such as that provided in an intensive care unit (ICU). ICU clinicians are …