  The task of assigning diagnostic ICD codes to patient hospital admissions is
typically performed by expert human coders. Efforts towards automated ICD
coding are dominated by supervised deep learning models. However, difficulties
in learning to predict the large number of rare codes remain a barrier to
adoption in clinical practice. In this work, we leverage off-the-shelf
pre-trained generative large language models (LLMs) to develop a practical
solution that is suitable for zero-shot and few-shot code assignment, with no
need for further task-specific training. Unsupervised pre-training alone does
not guarantee precise knowledge of the ICD ontology and specialist clinical
coding task, therefore we frame the task as information extraction, providing a
description of each coded concept and asking the model to retrieve related
mentions. For efficiency, rather than iterating over all codes, we leverage the
hierarchical nature of the ICD ontology to sparsely search for relevant codes.
