Medical documents classification using topic modeling
The number of digital medical documents is increasing continuously; several medical websites share a lot of unclassified articles. These articles have very long texts that should be read to determine the topic of each document. The classification of these documents is important so researchers can us...
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| Main Authors: | , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-03-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| LEADER | 02294 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs18823_13540 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Nuser, Maryam |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Al-Horani, Enas |e author |
| 245 | 0 | 0 | |a Medical documents classification using topic modeling |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18823 | ||
| 520 | |a The number of digital medical documents is increasing continuously; several medical websites share a lot of unclassified articles. These articles have very long texts that should be read to determine the topic of each document. The classification of these documents is important so researchers can use these documents easily and the effort and time in reading and searching for a specific topic will be reduced. Therefore, an automatic way to extract latent topics from these text documents is needed. Topic modeling is one of the techniques used to deal with this problem. In this paper, a medical collection of documents is used; this collection contains documents from three types of widespread diseases (Heart Diseases, Blood Pressure and Cholesterol). LDA topic modeling technique is applied to classify these documents into the previous mentioned topics. An evaluation of the algorithm's results is done and the LDA shows a good level of classification accuracy. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a computer science; health information systems;Information systems | ||
| 690 | |a Topic Modeling; Latent Dirichlet Allocation (LDA); Medical Documents; Classification; Mining Health Data. | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 17, No 3: March 2020; 1524-1530 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v17.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18823/13540 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/18823/13540 |z Get fulltext |