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Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models

Jonathan M Garibaldi, Shang-ming Zhou Orcid Logo, Xiao-Ying Wang, Robert I John, Ian O Ellis

Journal of Biomedical Informatics, Volume: 45, Issue: 3, Pages: 447 - 459

Swansea University Author: Shang-ming Zhou Orcid Logo

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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...

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Published in: Journal of Biomedical Informatics
ISSN: 1532-0464
Published: 2012
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URI: https://cronfa.swan.ac.uk/Record/cronfa13937
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spelling 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
container_volume 45
container_issue 3
container_start_page 447
publishDate 2012
institution Swansea University
issn 1532-0464
doi_str_mv 10.1016/j.jbi.2011.12.007
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 0
active_str 0
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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score 11.012678

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