CLINICAL NAMED ENTITY RECOGNITION PADA DATA BIOMEDIS MENGGUNAKAN MODEL BERT

QUR'AINI, KEISYAH SABINATULLAH and Firdaus, Firdaus and Tutuko, Bambang (2025) CLINICAL NAMED ENTITY RECOGNITION PADA DATA BIOMEDIS MENGGUNAKAN MODEL BERT. Undergraduate thesis, Sriwijaya University.

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Abstract

Named Entity Recognition (NER) is one of the key tasks in natural language processing (NLP), especially within the biomedical domain, which is complex and filled with specific terminologies. This research focuses on the application of a Clinical Named Entity Recognition model to extract biomedical entities from three datasets: BC2GM, JNLPBA, and NCBI-Disease. Several BERT-based model approaches are used, namely BERT, BERT combined with BiGRU (BERT-BiGRU), and BERT combined with Support Vector Machine (BERT-SVM). Performance evaluation is conducted using precision, recall, and F1-score metrics to assess the effectiveness of entity extraction. Experimental results show that the standard BERT model delivers the best performance on two datasets, BC2GM and NCBI-Disease, with F1-scores of 90% and 92%, respectively. Meanwhile, the BERT-BiGRU model achieves the best performance on the JNLPBA dataset, with an F1-score of 80%. These findings indicate that while BERT generally excels in understanding biomedical context and terminology, combining BERT with BiGRU can provide additional advantages in specific cases, such as with the JNLPBA dataset.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Clinical Named Entity Recogition, Biomedical Dataset, Transformer, BERT
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:	Keisyah Sabinatullah Qur'aini
Date Deposited:	20 Jun 2025 04:18
Last Modified:	20 Jun 2025 04:18
URI:	http://repository.unsri.ac.id/id/eprint/175808

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