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.

通过使用大规模语言模型与数据结合，本研究参与了一个在西班牙医学文档中检测症状、体征和发现的任务，旨在准确识别临床报告中的症状，并在医疗保健、生物医学自然语言处理领域发挥重要作用，为临床决策支持系统的开发和医疗专业人员的诊断和治疗规划提供帮助。