  Healthcare data suffers from both noise and lack of ground truth. The cost of
data increases as it is cleaned and annotated in healthcare. Unlike other data
sets, medical data annotation, which is critical to accurate ground truth,
requires medical domain expertise for a better patient outcome. In this work,
we report on the use of reinforcement learning to mimic the decision making
process of annotators for medical events, to automate annotation and labelling.
The reinforcement agent learns to annotate alarm data based on annotations done
by an expert. Our method shows promising results on medical alarm data sets. We
trained DQN and A2C agents using the data from monitoring devices annotated by
an expert. Initial results from these RL agents learning the expert annotation
behavior are promising. The A2C agent performs better in terms of learning the
sparse events in a given state, thereby choosing more right actions compared to
DQN agent. To the best of our knowledge, this is the first reinforcement
learning application for the automation of medical events annotation, which has
far-reaching practical use.
