  Clinical text provides essential information to estimate the acuity of a
patient during hospital stays in addition to structured clinical data. In this
study, we explore how clinical text can complement a clinical predictive
learning task. We leverage an internal medical natural language processing
service to perform named entity extraction and negation detection on clinical
notes and compose selected entities into a new text corpus to train document
representations. We then propose a multimodal neural network to jointly train
time series signals and unstructured clinical text representations to predict
the in-hospital mortality risk for ICU patients. Our model outperforms the
benchmark by 2% AUC.
