CLINICAL NAMED ENTITY RECOGNITION PADA DATA BIOMEDIS MENGGUNAKAN VARIAN MODEL BERT UNTUK KASUS BIOMEDIS

OKTARINA, TIARA and firdaus, firdaus (2025) CLINICAL NAMED ENTITY RECOGNITION PADA DATA BIOMEDIS MENGGUNAKAN VARIAN MODEL BERT UNTUK KASUS BIOMEDIS. Undergraduate thesis, Sriwijaya University.

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Abstract

This study aims to develop a system capable of identifying and classifying medical entities from unstructured biomedical texts, thereby supporting Clinical analysis and health research. The author trained and evaluated three BERT-based models: BioBERT, Clinical BERT, and BlueBERT, for the task of Clinical Named Entity Recognition (CNER). Model performance was measured using precision, recall, and F1-Score metrics on three biomedical datasets: NCBI, BC2GM, and JNLPBA. The results consistently show that BioBERT delivers the best performance across most datasets. On NCBI, BioBERT achieved the highest F1-Score of 93%, outperforming Clinical BERT and BlueBERT (both 91%). In BC2GM, BioBERT also excelled with 91%, while other models reached 90%. The JNLPBA dataset proved more challenging, with BioBERT achieving the highest F1-Score of only 80%. A significant performance improvement, up to 10% in some cases, was observed in the final experiments due to the application of full fine-tuning. This research contributes to identifying the most optimal transformer model for automated information extraction applications in healthcare.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Clinical Named Entity Recognition, Dataset Biomedic, Transformer, BioBERT, Clinical BERT, BlueBERT
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Tiara Oktarina
Date Deposited:	21 Jul 2025 13:21
Last Modified:	21 Jul 2025 13:21
URI:	http://repository.unsri.ac.id/id/eprint/179473

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