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Past studies on the ICD coding problem focus on predicting clinical codesprimarily based on the discharge summary. This covers only a small fraction ofthe notes generated during each hospital stay and leaves potential forimproving performance by analysing all the available clinical notes. We proposea hierarchical transformer architecture that uses text across the entiresequence of clinical notes in each hospital stay for ICD coding, andincorporates embeddings for text metadata such as their position, time, andtype of note. While using all clinical notes increases the quantity of datasubstantially, superconvergence can be used to reduce training costs. Weevaluate the model on the MIMIC-III dataset. Our model exceeds the priorstate-of-the-art when using only discharge summaries as input, and achievesfurther performance improvements when all clinical notes are used as input.

Modelling Temporal Document Sequences for Clinical ICD Coding