  Automated medical coding, an essential task for healthcare operation and
delivery, makes unstructured data manageable by predicting medical codes from
clinical documents. Recent advances in deep learning and natural language
processing have been widely applied to this task. However, deep learning-based
medical coding lacks a unified view of the design of neural network
architectures. This review proposes a unified framework to provide a general
understanding of the building blocks of medical coding models and summarizes
recent advanced models under the proposed framework. Our unified framework
decomposes medical coding into four main components, i.e., encoder modules for
text feature extraction, mechanisms for building deep encoder architectures,
decoder modules for transforming hidden representations into medical codes, and
the usage of auxiliary information. Finally, we introduce the benchmarks and
real-world usage and discuss key research challenges and future directions.
