No Cover Image

Journal article 559 views 118 downloads

Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective

Shang-ming Zhou Orcid Logo, Ronan Lyons Orcid Logo, Muhammad A. Rahman, Alexander Holborow, Sinead Brophy Orcid Logo

Journal of Personalized Medicine, Volume: 12, Issue: 1, Start page: 86

Swansea University Authors: Shang-ming Zhou Orcid Logo, Ronan Lyons Orcid Logo, Sinead Brophy Orcid Logo

  • jpm-12-00086.pdf

    PDF | Version of Record

    Copyright: © 2022 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

    Download (1.72MB)

Check full text

DOI (Published version): 10.3390/jpm12010086

Abstract

(1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990−2015). An approach called TF-zR (term...

Full description

Published in: Journal of Personalized Medicine
ISSN: 2075-4426
Published: MDPI AG 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa59184
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-01-14T19:38:34Z
last_indexed 2022-02-17T04:26:22Z
id cronfa59184
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-02-16T12:52:18.3350369</datestamp><bib-version>v2</bib-version><id>59184</id><entry>2022-01-14</entry><title>Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective</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>83efcf2a9dfcf8b55586999d3d152ac6</sid><ORCID>0000-0001-5225-000X</ORCID><firstname>Ronan</firstname><surname>Lyons</surname><name>Ronan Lyons</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>84f5661b35a729f55047f9e793d8798b</sid><ORCID>0000-0001-7417-2858</ORCID><firstname>Sinead</firstname><surname>Brophy</surname><name>Sinead Brophy</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-01-14</date><deptcode>BMS</deptcode><abstract>(1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990&#x2212;2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21&#x2212;25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis.</abstract><type>Journal Article</type><journal>Journal of Personalized Medicine</journal><volume>12</volume><journalNumber>1</journalNumber><paginationStart>86</paginationStart><paginationEnd/><publisher>MDPI AG</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2075-4426</issnElectronic><keywords>hospitalisation, readmission, Campylobacter infections, machine learning, text mining, feature selection, electronic health records</keywords><publishedDay>10</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-01-10</publishedDate><doi>10.3390/jpm12010086</doi><url/><notes/><college>COLLEGE NANME</college><department>Biomedical Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BMS</DepartmentCode><institution>Swansea University</institution><apcterm>SU College/Department paid the OA fee</apcterm><funders>This work was supported by the Health Data Research UK (NIWA1). This analysis was also undertaken with the support of the National Centre for Population Health and Wellbeing Research (NCPHWR) via Health and Care Research Wales (grant ref. CA02).</funders><lastEdited>2022-02-16T12:52:18.3350369</lastEdited><Created>2022-01-14T19:35:05.4102867</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Shang-ming</firstname><surname>Zhou</surname><orcid>0000-0002-0719-9353</orcid><order>1</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><orcid>0000-0001-5225-000X</orcid><order>2</order></author><author><firstname>Muhammad A.</firstname><surname>Rahman</surname><order>3</order></author><author><firstname>Alexander</firstname><surname>Holborow</surname><order>4</order></author><author><firstname>Sinead</firstname><surname>Brophy</surname><orcid>0000-0001-7417-2858</orcid><order>5</order></author></authors><documents><document><filename>59184__22147__e5404844a86a4daea92f318c05da2c01.pdf</filename><originalFilename>jpm-12-00086.pdf</originalFilename><uploaded>2022-01-14T19:35:05.4098894</uploaded><type>Output</type><contentLength>1802259</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: &#xA9; 2022 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-02-16T12:52:18.3350369 v2 59184 2022-01-14 Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 84f5661b35a729f55047f9e793d8798b 0000-0001-7417-2858 Sinead Brophy Sinead Brophy true false 2022-01-14 BMS (1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990−2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21−25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis. Journal Article Journal of Personalized Medicine 12 1 86 MDPI AG 2075-4426 hospitalisation, readmission, Campylobacter infections, machine learning, text mining, feature selection, electronic health records 10 1 2022 2022-01-10 10.3390/jpm12010086 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University SU College/Department paid the OA fee This work was supported by the Health Data Research UK (NIWA1). This analysis was also undertaken with the support of the National Centre for Population Health and Wellbeing Research (NCPHWR) via Health and Care Research Wales (grant ref. CA02). 2022-02-16T12:52:18.3350369 2022-01-14T19:35:05.4102867 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Shang-ming Zhou 0000-0002-0719-9353 1 Ronan Lyons 0000-0001-5225-000X 2 Muhammad A. Rahman 3 Alexander Holborow 4 Sinead Brophy 0000-0001-7417-2858 5 59184__22147__e5404844a86a4daea92f318c05da2c01.pdf jpm-12-00086.pdf 2022-01-14T19:35:05.4098894 Output 1802259 application/pdf Version of Record true Copyright: © 2022 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
spellingShingle Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
Shang-ming Zhou
Ronan Lyons
Sinead Brophy
title_short Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
title_full Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
title_fullStr Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
title_full_unstemmed Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
title_sort Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
author_id_str_mv 118578a62021ba8ef61398da0a8750da
83efcf2a9dfcf8b55586999d3d152ac6
84f5661b35a729f55047f9e793d8798b
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou
83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons
84f5661b35a729f55047f9e793d8798b_***_Sinead Brophy
author Shang-ming Zhou
Ronan Lyons
Sinead Brophy
author2 Shang-ming Zhou
Ronan Lyons
Muhammad A. Rahman
Alexander Holborow
Sinead Brophy
format Journal article
container_title Journal of Personalized Medicine
container_volume 12
container_issue 1
container_start_page 86
publishDate 2022
institution Swansea University
issn 2075-4426
doi_str_mv 10.3390/jpm12010086
publisher MDPI AG
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description (1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990−2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21−25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis.
published_date 2022-01-10T04:16:17Z
_version_ 1763754091948802048
score 11.036837

Similar Items