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Prediction of medical codes from clinical notes is both a practical andessential need for every healthcare delivery organization within currentmedical systems. Automating annotation will save significant time and excessiveeffort spent by human coders today. However, the biggest challenge is directlyidentifying appropriate medical codes out of several thousands ofhigh-dimensional codes from unstructured free-text clinical notes. In the pastthree years, with Convolutional Neural Networks (CNN) and Long Short-TermMemory (LTSM) networks, there have been vast improvements in tackling the mostchallenging benchmark of the MIMIC-III-full-label inpatient clinical notesdataset. This progress raises the fundamental question of how far automatedmachine learning (ML) systems are from human coders' working performance. Weassessed the baseline of human coders' performance on the same subsampledtesting set. We also present our Read, Attend, and Code (RAC) model forlearning the medical code assignment mappings. By connecting convolvedembeddings with self-attention and code-title guided attention modules,combined with sentence permutation-based data augmentations and stochasticweight averaging training, RAC establishes a new state of the art (SOTA),considerably outperforming the current best Macro-F1 by 18.7%, and reaches pastthe human-level coding baseline. This new milestone marks a meaningful steptoward fully autonomous medical coding (AMC) in machines reaching parity withhuman coders' performance in medical code prediction.

Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines