  We developed an explainable artificial intelligence (AI) early warning score
(xAI-EWS) system for early detection of acute critical illness. While
maintaining a high predictive performance, our system explains to the clinician
on which relevant electronic health records (EHRs) data the prediction is
grounded. Acute critical illness is often preceded by deterioration of
routinely measured clinical parameters, e.g., blood pressure and heart rate.
Early clinical prediction is typically based on manually calculated screening
metrics that simply weigh these parameters, such as Early Warning Scores (EWS).
The predictive performance of EWSs yields a tradeoff between sensitivity and
specificity that can lead to negative outcomes for the patient. Previous work
on EHR-trained AI systems offers promising results with high levels of
predictive performance in relation to the early, real-time prediction of acute
critical illness. However, without insight into the complex decisions by such
system, clinical translation is hindered. In this letter, we present our
xAI-EWS system, which potentiates clinical translation by accompanying a
prediction with information on the EHR data explaining it.

Artificial Intelligence;Machine Learning;Applications;Machine Learning