Summary of Fine-tuned Large Language Models For Symptom Recognition From Spanish Clinical Text, by Mai A. Shaaban et al.
Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text
by Mai A. Shaaban, Abbas Akkasi, Adnan Khan, Majid Komeili, Mohammad Yaqub
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the crucial challenge of accurately identifying symptoms in clinical reports, a fundamental step for healthcare professionals to extract critical insights from vast amounts of textual data. The authors focus on detecting symptoms, signs, and findings in Spanish medical documents as part of the SympTEMIST shared task. By fine-tuning large language models with the provided dataset, they aim to improve the detection of these entities, ultimately enabling advanced clinical decision support systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors understand what’s wrong with patients by reading their reports. They need to find specific words that say things like “headache” or “fever.” This is important because it helps them make better decisions about treatment. The researchers took part in a big project where they tried to do this automatically, using special computer models. They used these models to look at lots of medical documents written in Spanish and see if they could find the right words. |
