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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/2401.15780 |
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| _version_ | 1866914656181813248 |
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| author | Shaaban, Mai A. Akkasi, Abbas Khan, Adnan Komeili, Majid Yaqub, Mohammad |
| author_facet | Shaaban, Mai A. Akkasi, Abbas Khan, Adnan Komeili, Majid Yaqub, Mohammad |
| contents | The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing. These entities serve as essential building blocks for clinical information extraction, enabling retrieval of critical medical insights from vast amounts of textual data. Furthermore, the ability to identify and categorize these entities is fundamental for developing advanced clinical decision support systems, aiding healthcare professionals in diagnosis and treatment planning. In this study, we participated in SympTEMIST, a shared task on the detection of symptoms, signs and findings in Spanish medical documents. We combine a set of large language models fine-tuned with the data released by the organizers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15780 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text Shaaban, Mai A. Akkasi, Abbas Khan, Adnan Komeili, Majid Yaqub, Mohammad Computation and Language Machine Learning The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing. These entities serve as essential building blocks for clinical information extraction, enabling retrieval of critical medical insights from vast amounts of textual data. Furthermore, the ability to identify and categorize these entities is fundamental for developing advanced clinical decision support systems, aiding healthcare professionals in diagnosis and treatment planning. In this study, we participated in SympTEMIST, a shared task on the detection of symptoms, signs and findings in Spanish medical documents. We combine a set of large language models fine-tuned with the data released by the organizers. |
| title | Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2401.15780 |