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:过去关于ICD编码问题的研究侧重于主要根据排放摘要预测临床编码,只涵盖每家医院停留期间生成的笔记的一小部分,通过分析所有现有的临床笔记有可能提高性能。我们建议一个等级变压器结构,在每家医院停留期间使用整个系列临床笔记的文本进行 ICD 编码,并纳入文本元数据的嵌入,例如其位置、时间和注释类型。虽然所有临床笔记都大大增加了数据的数量,但超异性可用于减少培训费用。我们评估MIMIMIC-III 数据集的模型。我们的模式在仅使用排放摘要作为输入时超过了以往的先进水平,并在使用所有临床笔记作为输入时实现进一步的性能改进。</s>