A General Knowledge Injection Framework for ICD Coding (2025)

Abstract: ICD Coding aims to assign a wide range of medical codes to a medical text document, which is a popular and challenging task in the healthcare domain. To alleviate the problems of long-tail distribution and the lack of annotations of code-specific evidence, many previous works have proposed incorporating code knowledge to improve coding performance. However, existing methods often focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other, thereby limiting their scalability and effectiveness. To address this issue, we propose GKI-ICD, a novel, general knowledge injection framework that integrates three key types of knowledge, namely ICD Description, ICD Synonym, and ICD Hierarchy, without specialized design of additional modules. The comprehensive utilization of the above knowledge, which exhibits both differences and complementarity, can effectively enhance the ICD coding performance. Extensive experiments on existing popular ICD coding benchmarks demonstrate the effectiveness of GKI-ICD, which achieves the state-of-the-art performance on most evaluation metrics. Code is available at https://github.com/xuzhang0112/GKI-ICD.

## introduction international classification of diseases (icd) 1 is a globally used medical classification system, developed by the world health organization to classify diseases, symptoms, procedures, and external causes. the icd coding task aims to assign the most accurate icd codes to clinical texts, typically discharge summaries, for further medical billing and clinical research. two main challenges arise in the icd coding process @xcite . first, there is a tremendous number of icd codes to as-assigned icd codes (ground-truth) 038.9 unspecified septicemia 995.92 severe sepsis 96.71 continuous invasive mechanical ventilation for less than 96 consecutive hours ## methodology we first provide an overview in section 3.1, highlighting the key differences between our proposed gki-icd and previous works. next, we elaborate on its details in section 3.2. in addition, we briefly describe the icd coding network adopted in our work in section 3.3. ## conclusion in this paper, we propose gki-icd, a novel, general knowledge injection framework, which integrates multiple kinds of icd code knowledge for guideline synthesis and inject code knowledge to the icd coding model via multi-task learning. experimental results demonstrate that our proposed method outperforms the baseline models and is even comparable to models relying on extra human annotations. in addition, our framework does not make any changes to model architecture, thus being easy to be applied to other multi-label classification problems, using label-specific knowledge to improve the performance on rare labels.