  Outcome prediction from clinical text can prevent doctors from overlooking
possible risks and help hospitals to plan capacities. We simulate patients at
admission time, when decision support can be especially valuable, and
contribute a novel admission to discharge task with four common outcome
prediction targets: Diagnoses at discharge, procedures performed, in-hospital
mortality and length-of-stay prediction. The ideal system should infer outcomes
based on symptoms, pre-conditions and risk factors of a patient. We evaluate
the effectiveness of language models to handle this scenario and propose
clinical outcome pre-training to integrate knowledge about patient outcomes
from multiple public sources. We further present a simple method to incorporate
ICD code hierarchy into the models. We show that our approach improves
performance on the outcome tasks against several baselines. A detailed analysis
reveals further strengths of the model, including transferability, but also
weaknesses such as handling of vital values and inconsistencies in the
underlying data.
