  Dynamic assessment of mortality risk in the intensive care unit (ICU) can be
used to stratify patients, inform about treatment effectiveness or serve as
part of an early-warning system. Static risk scoring systems, such as APACHE or
SAPS, have recently been supplemented with data-driven approaches that track
the dynamic mortality risk over time. Recent works have focused on enhancing
the information delivered to clinicians even further by producing full survival
distributions instead of point predictions or fixed horizon risks. In this
work, we propose a non-parametric ensemble model, Weighted Resolution Survival
Ensemble (WRSE), tailored to estimate such dynamic individual survival
distributions. Inspired by the simplicity and robustness of ensemble methods,
the proposed approach combines a set of binary classifiers spaced according to
a decay function reflecting the relevance of short-term mortality predictions.
Models and baselines are evaluated under weighted calibration and
discrimination metrics for individual survival distributions which closely
reflect the utility of a model in ICU practice. We show competitive results
with state-of-the-art probabilistic models, while greatly reducing training
time by factors of 2-9x.
