Skripsi

CLINICAL NAMED ENTITY RECOGNITION PADA DATA BIOMEDIS MENGGUNAKAN MODEL BIDIRECTIONAL LONG SHORT TERM MEMORY - CONDITIONAL RANDOM FIELD

---

Penilaian

---

Advancements in Natural Language Processing (NLP) have improved the extraction of information from unstructured biomedical text, particularly in recognizing clinical named entities like diseases, genes, and proteins. This study evaluates the performance of Bi-LSTM and Bi-LSTM-CRF models for Clinical Named Entity Recognition (CNER) using three benchmark datasets: NCBI-Disease, BC2GM, and JNLPBA. It also investigates the effect of integrating GloVe word embeddings. Results show that Bi-LSTM generally outperforms Bi-LSTM-CRF in precision and recall, while Bi-LSTM-CRF maintains better label consistency. Evaluation is based on precision, recall, and F1-score, with findings supporting the development of more effective CNER models for clinical applications.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004158	T178860	T1788602025	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1788602025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 98 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem komputer

Specific Detail Info

-

Statement of Responsibility

TUTI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment