  Prediction of mortality in intensive care unit (ICU) patients is an important
task in critical care medicine. Prior work in creating mortality risk models
falls into two major categories: domain-expert-created scoring systems, and
black box machine learning (ML) models. Both of these have disadvantages: black
box models are unacceptable for use in hospitals, whereas manual creation of
models (including hand-tuning of logistic regression parameters) relies on
humans to perform high-dimensional constrained optimization, which leads to a
loss in performance. In this work, we bridge the gap between accurate black box
models and hand-tuned interpretable models. We build on modern interpretable ML
techniques to design accurate and interpretable mortality risk scores. We
leverage the largest existing public ICU monitoring datasets, namely the MIMIC
III and eICU datasets. By evaluating risk across medical centers, we are able
to study generalization across domains. In order to customize our risk score
models, we develop a new algorithm, GroupFasterRisk, which has several
important benefits: (1) it uses hard sparsity constraint, allowing users to
directly control the number of features; (2) it incorporates group sparsity to
allow more cohesive models; (3) it allows for monotonicity correction on models
for including domain knowledge; (4) it produces many equally-good models at
once, which allows domain experts to choose among them. GroupFasterRisk creates
its risk scores within hours, even on the large datasets we study here.
GroupFasterRisk's risk scores perform better than risk scores currently used in
hospitals, and have similar prediction performance to black box ML models
(despite being much sparser). Because GroupFasterRisk produces a variety of
risk scores and handles constraints, it allows design flexibility, which is the
key enabler of practical and trustworthy model creation.
