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Related Experiment Video

Updated: Jun 26, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Transformer-based approach for symptom recognition and multilingual linking.

Sylvia Vassileva¹, Georgi Grazhdanski¹, Ivan Koychev¹

¹Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Blvd "James Bourchier" 5, Sofia 1164, Bulgaria.

Database : the Journal of Biological Databases and Curation

|September 11, 2024

Summary

This summary is machine-generated.

This study introduces a transformer model for Spanish clinical Named Entity Recognition (NER) and multilingual entity linking, achieving high accuracy on the SympTEMIST dataset.

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Area of Science:

Natural Language Processing
Clinical Informatics
Biomedical Natural Language Processing

Background:

Named Entity Recognition (NER) and entity linking are crucial for extracting clinical information.
Existing methods may struggle with the nuances of clinical text and multilingual contexts.

Purpose of the Study:

To develop and evaluate a transformer-based approach for Spanish symptom NER.
To implement and assess a multilingual entity linking system for clinical texts.

Main Methods:

Fine-tuning a RoBERTa-based model with BiLSTM-CRF for Spanish NER.
Employing a hybrid dictionary-based approach with SapBERT and GPT-3.5 for multilingual entity linking.

Main Results:

Achieved an F1 score of 0.73 for Spanish symptom NER.
Obtained 0.73 accuracy for multilingual entity linking and 0.6123 for Spanish entity linking, surpassing previous benchmarks.

Conclusions:

The proposed transformer-based methods demonstrate strong performance in clinical NER and multilingual entity linking.
This approach enhances the extraction and standardization of clinical information across languages.