CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning
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..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
arXiv.org - (2024) vom: 23. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Luo, Junyu [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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| PPN (Katalog-ID): |
XAR042617057 |
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| 100 | 1 | |a Luo, Junyu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning |
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| 520 | |a 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. | ||
| 650 | 4 | |a Computer Science - Machine Learning |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Artificial Intelligence |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Computation and Language |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 000 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Wang, Xiaochen |4 aut | |
| 700 | 1 | |a Wang, Jiaqi |4 aut | |
| 700 | 1 | |a Chang, Aofei |4 aut | |
| 700 | 1 | |a Wang, Yaqing |4 aut | |
| 700 | 1 | |a Ma, Fenglong |4 aut | |
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