  Electronic Medical Records (EMR) are a rich source of patient information,
including measurements reflecting physiologic signs and administered therapies.
Identifying which variables are useful in predicting clinical outcomes can be
challenging. Advanced algorithms such as deep neural networks were designed to
process high-dimensional inputs containing variables in their measured form,
thus bypass separate feature selection or engineering steps. We investigated
the effect of extraneous input variables on the predictive performance of
Recurrent Neural Networks (RNN) by including in the input vector extraneous
variables randomly drawn from theoretical and empirical distributions. RNN
models using different input vectors (EMR variables; EMR and extraneous
variables; extraneous variables only) were trained to predict three clinical
outcomes: in-ICU mortality, 72-hour ICU re-admission, and 30-day ICU-free days.
The measured degradations of the RNN's predictive performance with the addition
of extraneous variables to EMR variables were negligible.
