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.

自动化医疗编码是医疗保健运营和交付的一项重要任务，它通过从临床文档中预测医疗代码来使非结构化数据变得易于管理。深度学习和自然语言处理的最新进展已广泛应用于此任务。然而，基于深度学习的医学编码缺乏神经网络架构设计的统一视图。本综述提出了一个统一的框架，以提供对医学编码模型构建块的一般理解，并总结了所提出框架下的最新先进模型。我们的统一框架将医学编码分解为四个主要组件，即用于文本特征提取的编码器模块、构建深度编码器架构的机制、用于将隐藏表示转换为医学代码的解码器模块以及辅助信息的使用。最后，我们介绍基准和实际用途，并讨论关键的研究挑战和未来方向。