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.

翻译：在临床报告中准确识别症状对于医疗健康领域和生物医学自然语言处理至关重要。这些实体作为临床信息提取的基本构建模块，能够从海量文本数据中检索关键医学见解。此外，识别和分类这些实体的能力对于开发先进的临床决策支持系统具有基础性意义，可辅助医疗专业人员进行诊断和治疗规划。在本研究中，我们参与了SympTEMIST共享任务，该任务专注于西班牙语医学文档中症状、体征与发现的检测。我们整合了一组使用组织者发布的数据进行微调的大语言模型。