Journal article 734 views
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models
Journal of Biomedical Informatics, Volume: 45, Issue: 3, Pages: 447 - 459
Swansea University Author: Shang-ming Zhou
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DOI (Published version): 10.1016/j.jbi.2011.12.007
Abstract
It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variabil...
Published in: | Journal of Biomedical Informatics |
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ISSN: | 1532-0464 |
Published: |
2012
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URI: | https://cronfa.swan.ac.uk/Record/cronfa13937 |
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<?xml version="1.0"?><rfc1807><datestamp>2019-07-17T12:05:30.9622272</datestamp><bib-version>v2</bib-version><id>13937</id><entry>2013-01-21</entry><title>Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models</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></swanseaauthors><date>2013-01-21</date><deptcode>BMS</deptcode><abstract>It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1–84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0–88.2%), p < 0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain.</abstract><type>Journal Article</type><journal>Journal of Biomedical Informatics</journal><volume>45</volume><journalNumber>3</journalNumber><paginationStart>447</paginationStart><paginationEnd>459</paginationEnd><publisher/><issnPrint>1532-0464</issnPrint><keywords>Breast cancer; Decision support; Expert systems; Fuzzy logic; Variability</keywords><publishedDay>30</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2012</publishedYear><publishedDate>2012-06-30</publishedDate><doi>10.1016/j.jbi.2011.12.007</doi><url/><notes/><college>COLLEGE NANME</college><department>Biomedical Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BMS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2019-07-17T12:05:30.9622272</lastEdited><Created>2013-01-21T11:13:00.1570599</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>Jonathan M</firstname><surname>Garibaldi</surname><order>1</order></author><author><firstname>Shang-ming</firstname><surname>Zhou</surname><orcid>0000-0002-0719-9353</orcid><order>2</order></author><author><firstname>Xiao-Ying</firstname><surname>Wang</surname><order>3</order></author><author><firstname>Robert I</firstname><surname>John</surname><order>4</order></author><author><firstname>Ian O</firstname><surname>Ellis</surname><order>5</order></author></authors><documents/><OutputDurs/></rfc1807> |
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2019-07-17T12:05:30.9622272 v2 13937 2013-01-21 Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2013-01-21 BMS It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1–84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0–88.2%), p < 0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain. Journal Article Journal of Biomedical Informatics 45 3 447 459 1532-0464 Breast cancer; Decision support; Expert systems; Fuzzy logic; Variability 30 6 2012 2012-06-30 10.1016/j.jbi.2011.12.007 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2019-07-17T12:05:30.9622272 2013-01-21T11:13:00.1570599 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Jonathan M Garibaldi 1 Shang-ming Zhou 0000-0002-0719-9353 2 Xiao-Ying Wang 3 Robert I John 4 Ian O Ellis 5 |
title |
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models |
spellingShingle |
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models Shang-ming Zhou |
title_short |
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models |
title_full |
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models |
title_fullStr |
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models |
title_full_unstemmed |
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models |
title_sort |
Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models |
author_id_str_mv |
118578a62021ba8ef61398da0a8750da |
author_id_fullname_str_mv |
118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou |
author |
Shang-ming Zhou |
author2 |
Jonathan M Garibaldi Shang-ming Zhou Xiao-Ying Wang Robert I John Ian O Ellis |
format |
Journal article |
container_title |
Journal of Biomedical Informatics |
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45 |
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447 |
publishDate |
2012 |
institution |
Swansea University |
issn |
1532-0464 |
doi_str_mv |
10.1016/j.jbi.2011.12.007 |
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Faculty of Medicine, Health and Life Sciences |
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Faculty of Medicine, Health and Life Sciences |
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facultyofmedicinehealthandlifesciences |
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Faculty of Medicine, Health and Life Sciences |
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Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine |
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description |
It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1–84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0–88.2%), p < 0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain. |
published_date |
2012-06-30T03:15:56Z |
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1763750294995337216 |
score |
11.017731 |