Fine-Tuned Large Language Models for Symptom Recognition from Spanish
  Clinical Text
Mai A. Shaaban Abbas Akkasi Adnan Khan Majid Komeili Mohammad Yaqub
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.