CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910343158038528 |
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| author | Luo, Junyu Wang, Xiaochen Wang, Jiaqi Chang, Aofei Wang, Yaqing Ma, Fenglong |
| author_facet | Luo, Junyu Wang, Xiaochen Wang, Jiaqi Chang, Aofei Wang, Yaqing Ma, Fenglong |
| contents | Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach, unlike existing methods, employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. We evaluate our approach on six public ICD coding datasets and the experimental results demonstrate the effectiveness of our approach compared to state-of-the-art baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning Luo, Junyu Wang, Xiaochen Wang, Jiaqi Chang, Aofei Wang, Yaqing Ma, Fenglong Machine Learning Artificial Intelligence Computation and Language Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach, unlike existing methods, employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. We evaluate our approach on six public ICD coding datasets and the experimental results demonstrate the effectiveness of our approach compared to state-of-the-art baselines. |
| title | CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2402.15700 |