HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding (2020.acl-main)
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| Challenge: | Existing methods for ICD coding ignore Code Hierarchy and Code Co-occurrence . cost of manual coding estimated to be $25 billion per year in the US . |
| Approach: | They propose a hyperbolic representation method to leverage the code hierarchy and a graph convolutional network to utilize the code co-occurrence. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two widely used datasets. |
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| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
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| Challenge: | Existing studies did not exploit the discourse structure of clinical notes, which provides rich contextual information for code assignment. |
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| Challenge: | Existing methods for ICD coding ignore the long-tail of code frequency or noisy clinical notes. |
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| Challenge: | Diagnostic coding is the task of assigning diagnosis codes defined by the ICD (International Classification of Diseases) standard to patient visits based on clinical notes. |
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| Challenge: | Medical coding is time-consuming, expensive, and error prone. |
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| Challenge: | Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. |
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| Challenge: | Existing approaches to assigning ICD codes to clinical text are time-consuming, labor intensive, and error-prone. |
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| Challenge: | Accurate International Classification of Diseases (ICD) coding is crucial for hospital management and healthcare data governance. |
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Fusion: Towards Automated ICD Coding via Feature Compression (2021.findings-acl)
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| Challenge: | Existing methods to assign ICD codes from unstructured clinical notes are noisy and prone to errors. |
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