New Arabic Medical Dataset for Diseases Classification

• Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2021 Vol. 13113; pp. 196 - 203
• Main Authors: Hammoud, Jaafar, Vatian, Aleksandra, Dobrenko, Natalia, Vedernikov, Nikolai, Shalyto, Anatoly, Gusarova, Natalia
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Arabic; Medical; Text classification
• ISBN: 3030916073; 9783030916077
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-91608-4_20

The Arabic language suffers from a great shortage of datasets suitable for training deep learning models, and the existing ones include general non-specialized classifications. In this work, we introduce a new Arab medical dataset, which includes two thousand medical documents collected from several Arabic medical websites, in addition to the Arab Medical Encyclopedia. The dataset was built for the task of classifying texts and includes 10 classes (Blood, Bone, Cardiovascular, Ear, Endocrine, Eye, Gastrointestinal, Immune, Liver and Nephrological) diseases. Experiments on the dataset were performed by fine-tuning three pre-trained models: BERT from Google, Arabert that based on BERT with large Arabic corpus, and AraBioNER that based on Arabert with Arabic medical corpus.