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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.06823 |
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| _version_ | 1866917833613508608 |
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| author | Boukhers, Zeyd Khan, AmeerAli Ramadan, Qusai Yang, Cong |
| author_facet | Boukhers, Zeyd Khan, AmeerAli Ramadan, Qusai Yang, Cong |
| contents | Addressing the complexity of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries is challenging due to the intricate nature of medical documentation. This paper explores the use of Large Language Models (LLM), specifically the LLAMA architecture, to enhance ICD code classification through two methodologies: direct application as a classifier and as a generator of enriched text representations within a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) framework. We evaluate these methods by comparing them against state-of-the-art approaches, revealing LLAMA's potential to significantly improve classification outcomes by providing deep contextual insights into medical texts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06823 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding Boukhers, Zeyd Khan, AmeerAli Ramadan, Qusai Yang, Cong Machine Learning Information Retrieval Addressing the complexity of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries is challenging due to the intricate nature of medical documentation. This paper explores the use of Large Language Models (LLM), specifically the LLAMA architecture, to enhance ICD code classification through two methodologies: direct application as a classifier and as a generator of enriched text representations within a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) framework. We evaluate these methods by comparing them against state-of-the-art approaches, revealing LLAMA's potential to significantly improve classification outcomes by providing deep contextual insights into medical texts. |
| title | Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2411.06823 |