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Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections / Shang-ming Zhou, Rahman A Muhammad, Samuel Sheppard, Robin Howe, Ronan A Lyons, Sinead Brophy

The Lancet, Volume: 390, Start page: S99

Swansea University Author: Shang-ming Zhou

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Published in: The Lancet
ISSN: 01406736
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa49928
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first_indexed 2019-04-09T13:04:49Z
last_indexed 2019-06-05T11:05:52Z
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spelling 2019-05-27T17:16:30.7507098 v2 49928 2019-04-08 Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2019-04-08 BMS Conference Paper/Proceeding/Abstract The Lancet 390 S99 01406736 31 12 2017 2017-12-31 10.1016/S0140-6736(17)33034-9 Meeting Abstract COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2019-05-27T17:16:30.7507098 2019-04-08T10:11:56.3805779 Swansea University Medical School Medicine Shang-ming Zhou 0000-0002-0719-9353 1 Rahman A Muhammad 2 Samuel Sheppard 3 Robin Howe 4 Ronan A Lyons 5 Sinead Brophy 6
title Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
spellingShingle Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
Shang-ming, Zhou
title_short Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
title_full Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
title_fullStr Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
title_full_unstemmed Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
title_sort Mining electronic health records to identify predictive factors associated with hospital admission for Campylobacter infections
author_id_str_mv 118578a62021ba8ef61398da0a8750da
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming, Zhou
author Shang-ming, Zhou
author2 Shang-ming Zhou
Rahman A Muhammad
Samuel Sheppard
Robin Howe
Ronan A Lyons
Sinead Brophy
format Conference Paper/Proceeding/Abstract
container_title The Lancet
container_volume 390
container_start_page S99
publishDate 2017
institution Swansea University
issn 01406736
doi_str_mv 10.1016/S0140-6736(17)33034-9
college_str Swansea University Medical School
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hierarchy_top_id swanseauniversitymedicalschool
hierarchy_top_title Swansea University Medical School
hierarchy_parent_id swanseauniversitymedicalschool
hierarchy_parent_title Swansea University Medical School
department_str Medicine{{{_:::_}}}Swansea University Medical School{{{_:::_}}}Medicine
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published_date 2017-12-31T04:13:08Z
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