AI Assisted Annotator using Reinforcement Learning
This addresses the costly need for medical expertise in data annotation for healthcare, though it appears incremental as it applies existing RL methods to a new domain.
The paper tackles the problem of high-cost and noisy healthcare data annotation by using reinforcement learning to mimic expert annotators for medical alarm events, showing that an A2C agent outperforms a DQN agent in learning sparse events and choosing correct actions.
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.