High model performance, on average, can hide that models may systematically
underperform on subgroups of the data. We consider the tabular setting, which surfaces the …

Observational medical data present unique opportunities for analysis of medical outcomes
and treatment decision making. However, because these datasets do not contain the strict …

Medical events of interest, such as mortality, often happen at a low rate in electronic medical
records, as most admitted patients survive. Training models with this imbalance rate (class …

Cardiovascular disease remains a leading cause of mortality with an estimated half a billion
people affected in 2019. However, detecting signals between specific pathophysiology and …

Background and objective: Survival analysis is widely applied for assessing the expected
duration of patient status towards event occurrences such as mortality in healthcare domain …

Hospitalized patients sometimes have complex health conditions, such as multiple diseases,
underlying diseases, and complications. The heterogeneous patient conditions may have …

In this work, we propose to use the Gated Mixture of Experts to interpret a deep learning
model trained on industrial data. Unlike monolithic deep learning models, gated modular …

With the development of biomedical sensing techniques and data storage, machine learning
has been widely applied to many healthcare applications from the abundance of data …

Intensive care unit (ICU) is an expensive and limited resource in hospitals, which majorly
maintains the survival of critical life-threatened patients. Accordingly, the short-term mortality …

The utility of machine learning for enhancing human well-being and health has risen to the
core discussion in both research and real-world application in today's technological front …