Past studies on the ICD coding problem focus on predicting clinical codes primarily based on the discharge summary. This covers only a small fraction of the notes generated during each hospital stay and leaves potential for improving performance by analysing all the available clinical notes. We propose a hierarchical transformer architecture that uses text across the entire sequence of clinical notes in each hospital stay for ICD coding, and incorporates embeddings for text metadata such as their position, time, and type of note. While using all clinical notes increases the quantity of data substantially, superconvergence can be used to reduce training costs. We evaluate the model on the MIMIC-III dataset. Our model exceeds the prior state-of-the-art when using only discharge summaries as input, and achieves further performance improvements when all clinical notes are used as input.

本研究提出一种基于层次变压器架构的ICD编码方法，使用病人整个住院期间的所有临床笔记进行编码，并结合文本元数据嵌入，如位置，时间和笔记类型等，在使用MIMIC-III数据集进行测试时，模型在仅使用出院摘要作为输入时超越了以前的最佳表现，并在使用所有临床笔记作为输入时实现了进一步的表现提升。

为临床ICD编码建模时间文档序列