  Prediction of medical codes from clinical notes is both a practical and
essential need for every healthcare delivery organization within current
medical systems. Automating annotation will save significant time and excessive
effort spent by human coders today. However, the biggest challenge is directly
identifying appropriate medical codes out of several thousands of
high-dimensional codes from unstructured free-text clinical notes. In the past
three years, with Convolutional Neural Networks (CNN) and Long Short-Term
Memory (LTSM) networks, there have been vast improvements in tackling the most
challenging benchmark of the MIMIC-III-full-label inpatient clinical notes
dataset. This progress raises the fundamental question of how far automated
machine learning (ML) systems are from human coders' working performance. We
assessed the baseline of human coders' performance on the same subsampled
testing set. We also present our Read, Attend, and Code (RAC) model for
learning the medical code assignment mappings. By connecting convolved
embeddings with self-attention and code-title guided attention modules,
combined with sentence permutation-based data augmentations and stochastic
weight averaging training, RAC establishes a new state of the art (SOTA),
considerably outperforming the current best Macro-F1 by 18.7%, and reaches past
the human-level coding baseline. This new milestone marks a meaningful step
toward fully autonomous medical coding (AMC) in machines reaching parity with
human coders' performance in medical code prediction.
