Abstract
The Medical Subject Headings (MeSH) term search is typical data-gathering method in biomedical text mining. However, it has two problems: the allocation delay of the MeSH term and missing valuable literature sources. Since MeSH term allocation is performed by a human being, the allocation process has delay. In addition, even if a literature source was allocated with a MeSH term, there is a still the problem that valuable literature sources are missed during the data-gathering process. There are literature sources that are not indexed to the MeSH term of a keyword, even though it contains valuable information related to the MeSH term. The MeSH term search misses these valuable literature sources. In order to resolve these problems, we propose a novel method to gather rich data using a one-class support vector machine (SVM) and relevance rule. The term frequency-inverse document frequency (TF-IDF) and paragraph vector are examined as text vectorization methods with various parameters and relevance factors. We apply our method to lung cancer, prostate cancer, breast cancer, and Alzheimer's disease. As a result, up to 26% of keyword data and 35% of target data are gathered with high quality (a C-score of at least 0.948).
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4325-4331 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509018970 |
| DOIs | |
| Publication status | Published - 2017 Feb 6 |
| Event | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary Duration: 2016 Oct 9 → 2016 Oct 12 |
Other
| Other | 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 |
|---|---|
| Country | Hungary |
| City | Budapest |
| Period | 16/10/9 → 16/10/12 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Control and Optimization
- Human-Computer Interaction
Cite this
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GRiD : Gathering rich data from PubMed using one-class SVM. / Cha, Junbum; Kim, Jeongwoo; Park, Sanghyun.
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4325-4331 7844911.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - GRiD
T2 - Gathering rich data from PubMed using one-class SVM
AU - Cha, Junbum
AU - Kim, Jeongwoo
AU - Park, Sanghyun
PY - 2017/2/6
Y1 - 2017/2/6
N2 - The Medical Subject Headings (MeSH) term search is typical data-gathering method in biomedical text mining. However, it has two problems: the allocation delay of the MeSH term and missing valuable literature sources. Since MeSH term allocation is performed by a human being, the allocation process has delay. In addition, even if a literature source was allocated with a MeSH term, there is a still the problem that valuable literature sources are missed during the data-gathering process. There are literature sources that are not indexed to the MeSH term of a keyword, even though it contains valuable information related to the MeSH term. The MeSH term search misses these valuable literature sources. In order to resolve these problems, we propose a novel method to gather rich data using a one-class support vector machine (SVM) and relevance rule. The term frequency-inverse document frequency (TF-IDF) and paragraph vector are examined as text vectorization methods with various parameters and relevance factors. We apply our method to lung cancer, prostate cancer, breast cancer, and Alzheimer's disease. As a result, up to 26% of keyword data and 35% of target data are gathered with high quality (a C-score of at least 0.948).
AB - The Medical Subject Headings (MeSH) term search is typical data-gathering method in biomedical text mining. However, it has two problems: the allocation delay of the MeSH term and missing valuable literature sources. Since MeSH term allocation is performed by a human being, the allocation process has delay. In addition, even if a literature source was allocated with a MeSH term, there is a still the problem that valuable literature sources are missed during the data-gathering process. There are literature sources that are not indexed to the MeSH term of a keyword, even though it contains valuable information related to the MeSH term. The MeSH term search misses these valuable literature sources. In order to resolve these problems, we propose a novel method to gather rich data using a one-class support vector machine (SVM) and relevance rule. The term frequency-inverse document frequency (TF-IDF) and paragraph vector are examined as text vectorization methods with various parameters and relevance factors. We apply our method to lung cancer, prostate cancer, breast cancer, and Alzheimer's disease. As a result, up to 26% of keyword data and 35% of target data are gathered with high quality (a C-score of at least 0.948).
UR - http://www.scopus.com/inward/record.url?scp=85015752038&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015752038&partnerID=8YFLogxK
U2 - 10.1109/SMC.2016.7844911
DO - 10.1109/SMC.2016.7844911
M3 - Conference contribution
SP - 4325
EP - 4331
BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -