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.

翻译：既往针对ICD编码问题的研究主要基于出院小结进行临床代码预测。这仅覆盖每次住院期间所生成病历记录的一小部分，通过分析所有可用的临床记录仍有提升性能的潜力。我们提出了一种分层Transformer架构，该架构利用每次住院期间全部临床记录序列中的文本进行ICD编码，并融入文本元数据的嵌入表示（如位置、时间及记录类型）。尽管使用全部临床记录会显著增加数据量，但超收敛技术可有效降低训练成本。我们在MIMIC-III数据集上对该模型进行了评估。当仅以出院小结作为输入时，本模型已超越现有最优方法；而将全部临床记录作为输入时，模型性能获得进一步提升。