Papers with Automatic International Classification of Diseases

2 papers
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)

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Challenge: Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge.
Approach: They propose a prompt-based fine-tuning technique with label semantics to solve this challenge.
Outcome: The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases.
CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)

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Challenge: Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes .
Approach: They propose a contextualized and flexible framework to enhance learning of ICD code relations . they use clinical notes to model all possible code relations using a dependent learning paradigm .
Outcome: The proposed approach improves on six public ICD coding datasets compared to state-of-the-art models.

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