AI-Assisted Annotator Using Reinforcement Learning

Machine learning in the healthcare domain is often hindered by data which are both noisy and lacking reliable ground truth labeling. Moreover, the cost of cleaning and annotating this data is significant since, unlike other data domains, medical data annotation requires the work of skilled medical professionals. In this work, we introduced the use of reinforcement learning to mimic the decision-making process of annotators for medical events allowing automation of annotation and labeling. Our reinforcement agent learns to annotate health monitor alarm data based on annotations done by an expert. We demonstrate the efficacy of our implementation on ICU critical alarm data sets. We evaluate our algorithm against standard supervised machine learning and deep learning methods. Compared to SVM and LSTM methods, our method achieves high sensitivity that is critical for alarm data; exhibits better generalization across mixed downsampling; and preserves comparable model performance.