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Automated medical coding, an essential task for healthcare operation anddelivery, makes unstructured data manageable by predicting medical codes fromclinical documents. Recent advances in deep learning and natural languageprocessing have been widely applied to this task. However, deep learning-basedmedical coding lacks a unified view of the design of neural networkarchitectures. This review proposes a unified framework to provide a generalunderstanding of the building blocks of medical coding models and summarizesrecent advanced models under the proposed framework. Our unified frameworkdecomposes medical coding into four main components, i.e., encoder modules fortext feature extraction, mechanisms for building deep encoder architectures,decoder modules for transforming hidden representations into medical codes, andthe usage of auxiliary information. Finally, we introduce the benchmarks andreal-world usage and discuss key research challenges and future directions.

A Unified Review of Deep Learning for Automated Medical Coding