A bibliometric analysis of text mining in medical research
Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs o...
Gespeichert in:
| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2018-12, Vol.22 (23), p.7875-7892 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 7892 |
|---|---|
| container_issue | 23 |
| container_start_page | 7875 |
| container_title | Soft computing (Berlin, Germany) |
| container_volume | 22 |
| creator | Hao, Tianyong Chen, Xieling Li, Guozheng Yan, Jun |
| description | Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research. |
| doi_str_mv | 10.1007/s00500-018-3511-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2917948434</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2917948434</sourcerecordid><originalsourceid>FETCH-LOGICAL-c425t-388e748eaf4671081763ba84df92f5d5a5bbf95e1b507bab3bb72f51e36eed353</originalsourceid><addsrcrecordid>eNp1kE1LxDAQhoMouK7-AG8Bz9FMkzStt2XxCxa86Dkk7WTN0o816YL7722t4MnTDMzzvgwPIdfAb4FzfZc4V5wzDgUTCoDJE7IAKQTTUpenP3vGdC7FOblIacd5BlqJBblfURdcE_oWhxgqajvbHFNItPd0wK-BtqEL3ZaGjrZYh8o2NGJCG6uPS3LmbZPw6ncuyfvjw9v6mW1en17Wqw2rZKYGJooCtSzQeplr4AXoXDhbyNqXmVe1sso5XyoEp7h21gnn9HgAFDliLZRYkpu5dx_7zwOmwez6Qxz_TCYrQZeykEKOFMxUFfuUInqzj6G18WiAm0mRmRWZUZGZFJkpk82ZNLLdFuNf8_-hb58NaDM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2917948434</pqid></control><display><type>article</type><title>A bibliometric analysis of text mining in medical research</title><source>SpringerLink Journals</source><source>ProQuest Central</source><creator>Hao, Tianyong ; Chen, Xieling ; Li, Guozheng ; Yan, Jun</creator><creatorcontrib>Hao, Tianyong ; Chen, Xieling ; Li, Guozheng ; Yan, Jun</creatorcontrib><description>Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-018-3511-4</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial Intelligence ; Bibliometrics ; Citation indexes ; Collaboration ; Computational Intelligence ; Control ; Cooperation ; Data mining ; Discriminant analysis ; Engineering ; Focus ; Geographical distribution ; Interdisciplinary aspects ; Keywords ; Library and information science ; Mathematical Logic and Foundations ; Mechatronics ; Medical records ; Medical research ; Natural language processing ; Network analysis ; Optimization techniques ; Robotics ; Science ; Social networks</subject><ispartof>Soft computing (Berlin, Germany), 2018-12, Vol.22 (23), p.7875-7892</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-388e748eaf4671081763ba84df92f5d5a5bbf95e1b507bab3bb72f51e36eed353</citedby><cites>FETCH-LOGICAL-c425t-388e748eaf4671081763ba84df92f5d5a5bbf95e1b507bab3bb72f51e36eed353</cites><orcidid>0000-0002-9792-3949</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-018-3511-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2917948434?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Hao, Tianyong</creatorcontrib><creatorcontrib>Chen, Xieling</creatorcontrib><creatorcontrib>Li, Guozheng</creatorcontrib><creatorcontrib>Yan, Jun</creatorcontrib><title>A bibliometric analysis of text mining in medical research</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research.</description><subject>Artificial Intelligence</subject><subject>Bibliometrics</subject><subject>Citation indexes</subject><subject>Collaboration</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Cooperation</subject><subject>Data mining</subject><subject>Discriminant analysis</subject><subject>Engineering</subject><subject>Focus</subject><subject>Geographical distribution</subject><subject>Interdisciplinary aspects</subject><subject>Keywords</subject><subject>Library and information science</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Medical records</subject><subject>Medical research</subject><subject>Natural language processing</subject><subject>Network analysis</subject><subject>Optimization techniques</subject><subject>Robotics</subject><subject>Science</subject><subject>Social networks</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kE1LxDAQhoMouK7-AG8Bz9FMkzStt2XxCxa86Dkk7WTN0o816YL7722t4MnTDMzzvgwPIdfAb4FzfZc4V5wzDgUTCoDJE7IAKQTTUpenP3vGdC7FOblIacd5BlqJBblfURdcE_oWhxgqajvbHFNItPd0wK-BtqEL3ZaGjrZYh8o2NGJCG6uPS3LmbZPw6ncuyfvjw9v6mW1en17Wqw2rZKYGJooCtSzQeplr4AXoXDhbyNqXmVe1sso5XyoEp7h21gnn9HgAFDliLZRYkpu5dx_7zwOmwez6Qxz_TCYrQZeykEKOFMxUFfuUInqzj6G18WiAm0mRmRWZUZGZFJkpk82ZNLLdFuNf8_-hb58NaDM</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Hao, Tianyong</creator><creator>Chen, Xieling</creator><creator>Li, Guozheng</creator><creator>Yan, Jun</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-9792-3949</orcidid></search><sort><creationdate>20181201</creationdate><title>A bibliometric analysis of text mining in medical research</title><author>Hao, Tianyong ; Chen, Xieling ; Li, Guozheng ; Yan, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-388e748eaf4671081763ba84df92f5d5a5bbf95e1b507bab3bb72f51e36eed353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Artificial Intelligence</topic><topic>Bibliometrics</topic><topic>Citation indexes</topic><topic>Collaboration</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Cooperation</topic><topic>Data mining</topic><topic>Discriminant analysis</topic><topic>Engineering</topic><topic>Focus</topic><topic>Geographical distribution</topic><topic>Interdisciplinary aspects</topic><topic>Keywords</topic><topic>Library and information science</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Medical records</topic><topic>Medical research</topic><topic>Natural language processing</topic><topic>Network analysis</topic><topic>Optimization techniques</topic><topic>Robotics</topic><topic>Science</topic><topic>Social networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hao, Tianyong</creatorcontrib><creatorcontrib>Chen, Xieling</creatorcontrib><creatorcontrib>Li, Guozheng</creatorcontrib><creatorcontrib>Yan, Jun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hao, Tianyong</au><au>Chen, Xieling</au><au>Li, Guozheng</au><au>Yan, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A bibliometric analysis of text mining in medical research</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2018-12-01</date><risdate>2018</risdate><volume>22</volume><issue>23</issue><spage>7875</spage><epage>7892</epage><pages>7875-7892</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-018-3511-4</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-9792-3949</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1432-7643 |
| ispartof | Soft computing (Berlin, Germany), 2018-12, Vol.22 (23), p.7875-7892 |
| issn | 1432-7643 1433-7479 |
| language | eng |
| recordid | cdi_proquest_journals_2917948434 |
| source | SpringerLink Journals; ProQuest Central |
| subjects | Artificial Intelligence Bibliometrics Citation indexes Collaboration Computational Intelligence Control Cooperation Data mining Discriminant analysis Engineering Focus Geographical distribution Interdisciplinary aspects Keywords Library and information science Mathematical Logic and Foundations Mechatronics Medical records Medical research Natural language processing Network analysis Optimization techniques Robotics Science Social networks |
| title | A bibliometric analysis of text mining in medical research |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T23%3A50%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20bibliometric%20analysis%20of%20text%20mining%20in%20medical%20research&rft.jtitle=Soft%20computing%20(Berlin,%20Germany)&rft.au=Hao,%20Tianyong&rft.date=2018-12-01&rft.volume=22&rft.issue=23&rft.spage=7875&rft.epage=7892&rft.pages=7875-7892&rft.issn=1432-7643&rft.eissn=1433-7479&rft_id=info:doi/10.1007/s00500-018-3511-4&rft_dat=%3Cproquest_cross%3E2917948434%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2917948434&rft_id=info:pmid/&rfr_iscdi=true |