Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document

Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document

In recent years, the application of deep learning methods has become increasingly popular, especially for big data, because big data has a very large data size and needs to be predicted accurately. One of the big data is the document text data of cancer clinical trials. Clinical trials are studies o...

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Bibliographic Details
Main Authors: Jasmir Jasmir, Willy Riyadi, Silvia Rianti Agustini, Yulia Arvita, Despita Meisak, Lies Aryani
Format: Article
Language:English
Published: Ikatan Ahli Informatika Indonesia 2022-08-01
Series:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Subjects:
deep learning, bilstm, text classification, word embedding, clinical trials
Online Access:http://jurnal.iaii.or.id/index.php/RESTI/article/view/4005

Internet

http://jurnal.iaii.or.id/index.php/RESTI/article/view/4005

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