  This paper presents a reinforcement learning approach to extract noise in
long clinical documents for the task of readmission prediction after kidney
transplant. We face the challenges of developing robust models on a small
dataset where each document may consist of over 10K tokens with full of noise
including tabular text and task-irrelevant sentences. We first experiment four
types of encoders to empirically decide the best document representation, and
then apply reinforcement learning to remove noisy text from the long documents,
which models the noise extraction process as a sequential decision problem. Our
results show that the old bag-of-words encoder outperforms deep learning-based
encoders on this task, and reinforcement learning is able to improve upon
baseline while pruning out 25% text segments. Our analysis depicts that
reinforcement learning is able to identify both typical noisy tokens and
task-specific noisy text.
