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.
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. Then, in a second stage, which
we term 'meta-refinement', we utilise GPT-4 to select a subset of the relevant
labels as predictions. We validate our method using Llama-2, GPT-3.5 and GPT-4
on the CodiEsp dataset of ICD-coded clinical case documents. Our tree-search
method achieves state-of-the-art performance on rarer classes, achieving the
best macro-F1 of 0.225, whilst achieving slightly lower micro-F1 of 0.157,
compared to 0.216 and 0.219 respectively from PLM-ICD. To the best of our
knowledge, this is the first method for automated ICD coding requiring no
task-specific learning.

利用大型预训练生成语言模型开发出零样本和少样本编码分配的实用解决方案，通过信息提取，利用 ICD 本体论和专业临床编码任务描述，检索相关提及，并利用 GPT-4 在第二阶段进行元细化，实现了自动 ICD 编码的方法，无需任务特定的学习，而在更稀缺的类别上达到了最优的性能。