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

EventScore: An Automated Real-time Early Warning Score for Clinical Events