  Early recognition of clinical deterioration is one of the main steps for
reducing inpatient morbidity and mortality. The challenging task of clinical
deterioration identification in hospitals lies in the intense daily routines of
healthcare practitioners, in the unconnected patient data stored in the
Electronic Health Records (EHRs) and in the usage of low accuracy scores. Since
hospital wards are given less attention compared to the Intensive Care Unit,
ICU, we hypothesized that when a platform is connected to a stream of EHR,
there would be a drastic improvement in dangerous situations awareness and
could thus assist the healthcare team. With the application of machine
learning, the system is capable to consider all patient's history and through
the use of high-performing predictive models, an intelligent early warning
system is enabled. In this work we used 121,089 medical encounters from six
different hospitals and 7,540,389 data points, and we compared popular ward
protocols with six different scalable machine learning methods (three are
classic machine learning models, logistic and probabilistic-based models, and
three gradient boosted models). The results showed an advantage in AUC (Area
Under the Receiver Operating Characteristic Curve) of 25 percentage points in
the best Machine Learning model result compared to the current state-of-the-art
protocols. This is shown by the generalization of the algorithm with
leave-one-group-out (AUC of 0.949) and the robustness through cross-validation
(AUC of 0.961). We also perform experiments to compare several window sizes to
justify the use of five patient timestamps. A sample dataset, experiments, and
code are available for replicability purposes.
