با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Active deep learning for the identification of concepts and relations in electroencephalography reports
ترجمه فارسی عنوان مقاله:
یادگیری عمیق فعال برای شناسایی مفاهیم و روابط در گزارشات الکتروانسفالوگرافی
منبع:
Sciencedirect - Elsevier - Journal of Biomedical Informatics, 98 (2019) 103265: doi:10:1016/j:jbi:2019:103265
نویسنده:
Ramon Maldonado⁎, Sanda M. Harabagiu
چکیده انگلیسی:
The identification of medical concepts, their attributes and the relations between concepts in a large corpus of
Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort
retrieval system. However, the recognition of multiple types of medical concepts, along with the many attributes
characterizing them is challenging, and so is the recognition of the possible relations between them, especially
when desiring to make use of active learning. To address these challenges, in this paper we present the Self-
Attention Concept, Attribute and Relation (SACAR) identifier, which relies on a powerful encoding mechanism
based on the recently introduced Transformer neural architecture (Dehghani et al., 2018). The SACAR identifier
enabled us to consider a recently introduced framework for active learning which uses deep imitation learning
for its selection policy. Our experimental results show that SACAR was able to identify medical concepts more
precisely and exhibited enhanced recall, compared with previous methods. Moreover, SACAR achieves superior
performance in attribute classification for attribute categories of interest, while identifying the relations between
concepts with performance competitive with our previous techniques. As a multi-task network, SACAR achieves
this performance on the three prediction tasks simultaneously, with a single, complex neural network. The
learning curves obtained in the active learning process when using the novel Active Learning Policy Neural
Network (ALPNN) show a significant increase in performance as the active learning progresses. These promising
results enable the extraction of clinical knowledge available in a large collection of EEG reports.
Keywords: Deep learning | Electroencephalography | Active learning | Long-distance relation identification | Concept detection | Attribute classification
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0