KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
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TL;DR - KREL is an LLM-based framework for automatically assigning ICD codes to clinical notes by combining clinical-evidence reasoning with structured coding guidelines. This knowledge-guided approach aims to reduce hallucinations, improve standards compliance, and handle challenges such as long notes and a large label space.
- Integrates external ICD coding guidelines directly into the LLM reasoning process.
- Couples clinical-text interpretation with evidence-based, multi-step code selection.
- Addresses limitations of extreme multi-label classification and unconstrained generative approaches.
- Consistently outperforms strong pretrained language model and state-of-the-art LLM baselines on benchmark datasets.
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KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
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TL;DR - KREL is an LLM-based framework for automatically assigning ICD codes to clinical notes by combining clinical-evidence reasoning with structured coding guidelines. This knowledge-guided approach aims to reduce hallucinations, improve standards compliance, and handle challenges such as long notes and a large label space.
- Integrates external ICD coding guidelines directly into the LLM reasoning process.
- Couples clinical-text interpretation with evidence-based, multi-step code selection.
- Addresses limitations of extreme multi-label classification and unconstrained generative approaches.
- Consistently outperforms strong pretrained language model and state-of-the-art LLM baselines on benchmark datasets.