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.

翻译：将患者住院诊断分配国际疾病分类（ICD）代码的任务通常由专业人工编码员完成。自动化ICD编码的研究主要采用监督式深度学习模型，但在学习预测大量罕见代码方面存在的困难仍是其临床应用推广的障碍。本研究利用预训练的现成生成式大型语言模型（LLMs）开发实用解决方案，该方案适用于零样本和少样本代码分配，无需额外任务特定训练。无监督预训练本身无法保证模型精准掌握ICD本体论和专业化临床编码任务，因此我们将该任务重构为信息抽取问题：为每个编码概念提供描述，并引导模型检索相关提及内容。为提高效率，我们利用ICD本体论的层级结构对相关代码进行稀疏搜索，而非遍历所有代码。