  A language model can be used to predict the next word during authoring, to
correct spelling or to accelerate writing (e.g., in sms or emails). Language
models, however, have only been applied in a very small scale to assist
physicians during authoring (e.g., discharge summaries or radiology reports).
But along with the assistance to the physician, computer-based systems which
expedite the patient's exit also assist in decreasing the hospital infections.
We employed statistical and neural language modeling to predict the next word
of a clinical text and assess all the models in terms of accuracy and keystroke
discount in two datasets with radiology reports. We show that a neural language
model can achieve as high as 51.3% accuracy in radiology reports (one out of
two words predicted correctly). We also show that even when the models are
employed only for frequent words, the physician can save valuable time.
