Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach

Zhou, Shang-ming; Tsang, Gavin; Xie, Xianghua; Huo, Lin; Brophy, Sinead; Lyons, Ronan A

doi:10.1016/S0140-6736(18)32166-4

Journal article 1087 views 271 downloads

Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach

Shang-ming Zhou

, Gavin Tsang, Xianghua Xie

, Lin Huo, Sinead Brophy, Ronan A Lyons

The Lancet, Volume: 392, Start page: S9

Swansea University Authors: Shang-ming Zhou , Xianghua Xie

PDF | Accepted Manuscript

Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND).
Download (127.07KB)

Check full text

DOI (Published version): 10.1016/S0140-6736(18)32166-4

Published in:	The Lancet
ISSN:	01406736
Published:	2018
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa45957
Tags:	Add Tag No Tags, Be the first to tag this record!

first_indexed	2018-11-16T20:18:30Z
last_indexed	2019-02-11T11:55:33Z
id	cronfa45957
recordtype	SURis
fullrecord	<?xml version="1.0"?><rfc1807><datestamp>2019-02-07T09:21:10.0855149</datestamp><bib-version>v2</bib-version><id>45957</id><entry>2018-11-16</entry><title>Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach</title><swanseaauthors><author><sid>118578a62021ba8ef61398da0a8750da</sid><ORCID>0000-0002-0719-9353</ORCID><firstname>Shang-ming</firstname><surname>Zhou</surname><name>Shang-ming Zhou</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>b334d40963c7a2f435f06d2c26c74e11</sid><ORCID>0000-0002-2701-8660</ORCID><firstname>Xianghua</firstname><surname>Xie</surname><name>Xianghua Xie</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2018-11-16</date><deptcode>BMS</deptcode><abstract/><type>Journal Article</type><journal>The Lancet</journal><volume>392</volume><paginationStart>S9</paginationStart><publisher/><issnPrint>01406736</issnPrint><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-12-31</publishedDate><doi>10.1016/S0140-6736(18)32166-4</doi><url/><notes/><college>COLLEGE NANME</college><department>Biomedical Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BMS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2019-02-07T09:21:10.0855149</lastEdited><Created>2018-11-16T14:22:43.5395861</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Shang-ming</firstname><surname>Zhou</surname><orcid>0000-0002-0719-9353</orcid><order>1</order></author><author><firstname>Gavin</firstname><surname>Tsang</surname><order>2</order></author><author><firstname>Xianghua</firstname><surname>Xie</surname><orcid>0000-0002-2701-8660</orcid><order>3</order></author><author><firstname>Lin</firstname><surname>Huo</surname><order>4</order></author><author><firstname>Sinead</firstname><surname>Brophy</surname><order>5</order></author><author><firstname>Ronan A</firstname><surname>Lyons</surname><order>6</order></author></authors><documents><document><filename>0045957-07022019091712.pdf</filename><originalFilename>45957.pdf</originalFilename><uploaded>2019-02-07T09:17:12.4030000</uploaded><type>Output</type><contentLength>105392</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2019-02-06T00:00:00.0000000</embargoDate><documentNotes>Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling	2019-02-07T09:21:10.0855149 v2 45957 2018-11-16 Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2018-11-16 BMS Journal Article The Lancet 392 S9 01406736 31 12 2018 2018-12-31 10.1016/S0140-6736(18)32166-4 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2019-02-07T09:21:10.0855149 2018-11-16T14:22:43.5395861 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Shang-ming Zhou 0000-0002-0719-9353 1 Gavin Tsang 2 Xianghua Xie 0000-0002-2701-8660 3 Lin Huo 4 Sinead Brophy 5 Ronan A Lyons 6 0045957-07022019091712.pdf 45957.pdf 2019-02-07T09:17:12.4030000 Output 105392 application/pdf Accepted Manuscript true 2019-02-06T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title	Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach
spellingShingle	Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach Shang-ming Zhou Xianghua Xie
title_short	Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach
title_full	Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach
title_fullStr	Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach
title_full_unstemmed	Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach
title_sort	Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach
author_id_str_mv	118578a62021ba8ef61398da0a8750da b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv	118578a62021ba8ef61398da0a8750da_*_Shang-ming Zhou b334d40963c7a2f435f06d2c26c74e11_*_Xianghua Xie
author	Shang-ming Zhou Xianghua Xie
author2	Shang-ming Zhou Gavin Tsang Xianghua Xie Lin Huo Sinead Brophy Ronan A Lyons
format	Journal article
container_title	The Lancet
container_volume	392
container_start_page	S9
publishDate	2018
institution	Swansea University
issn	01406736
doi_str_mv	10.1016/S0140-6736(18)32166-4
college_str	Faculty of Science and Engineering
hierarchytype
hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str	1
active_str	0
published_date	2018-12-31T03:57:38Z
_version_	1763752918049095680
score	11.036684

Mining electronic health records to identify influential predictors associated with hospital admission of patients with dementia: an artificial intelligence approach

Similar Items