  Predictive models in acute care settings must be able to immediately
recognize precipitous changes in a patient's status when presented with data
reflecting such changes. Recurrent neural networks (RNNs) have become common
for training and deploying clinical decision support models. They frequently
exhibit a delayed response to acute events. New information must propagate
through the RNN's cell state memory before the total impact is reflected in the
model's predictions. This work presents input data perseveration as a method of
training and deploying an RNN model to make its predictions more responsive to
newly acquired information: input data is replicated during training and
deployment. Each replication of the data input impacts the cell state and
output of the RNN, but only the output at the final replication is maintained
and broadcast as the prediction for evaluation and deployment purposes. When
presented with data reflecting acute events, a model trained and deployed with
input perseveration responds with more pronounced immediate changes in
predictions and maintains globally robust performance. Such a characteristic is
crucial in predictive models for an intensive care unit.
