  Early prediction of patients at risk of clinical deterioration can help
physicians intervene and alter their clinical course towards better outcomes.
In addition to the accuracy requirement, early warning systems must make the
predictions early enough to give physicians enough time to intervene.
Interpretability is also one of the challenges when building such systems since
being able to justify the reasoning behind model decisions is desirable in
clinical practice. In this work, we built an interpretable model for the early
prediction of various adverse clinical events indicative of clinical
deterioration. The model is evaluated on two datasets and four clinical events.
The first dataset is collected in a predominantly COVID-19 positive population
at Stony Brook Hospital. The second dataset is the MIMIC III dataset. The model
was trained to provide early warning scores for ventilation, ICU transfer, and
mortality prediction tasks on the Stony Brook Hospital dataset and to predict
mortality and the need for vasopressors on the MIMIC III dataset. Our model
first separates each feature into multiple ranges and then uses logistic
regression with lasso penalization to select the subset of ranges for each
feature. The model training is completely automated and doesn't require expert
knowledge like other early warning scores. We compare our model to the Modified
Early Warning Score (MEWS) and quick SOFA (qSOFA), commonly used in hospitals.
We show that our model outperforms these models in the area under the receiver
operating characteristic curve (AUROC) while having a similar or better median
detection time on all clinical events, even when using fewer features. Unlike
MEWS and qSOFA, our model can be entirely automated without requiring any
manually recorded features. We also show that discretization improves model
performance by comparing our model to a baseline logistic regression model.
