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

Jonathan M. Garibaldi; Shang Ming Zhou; Xiao Ying Wang; Robert I. John; Ian O. Ellis

doi:10.1016/j.jbi.2011.12.007

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

Jonathan M. Garibaldi
, Shang Ming Zhou^*
, Xiao Ying Wang
, Robert I. John
, Ian O. Ellis

^*Corresponding author for this work

School of Nursing and Midwifery

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	447-459
Number of pages	0
Journal	Journal of Biomedical Informatics
Volume	45
Issue number	3
DOIs	https://doi.org/10.1016/j.jbi.2011.12.007
Publication status	Published - Jun 2012

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10.1016/j.jbi.2011.12.007

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@article{51f4e13b40a140ff8ab146417dc068ed,

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

author = "Garibaldi, \{Jonathan M.\} and Zhou, \{Shang Ming\} and Wang, \{Xiao Ying\} and John, \{Robert I.\} and Ellis, \{Ian O.\}",

year = "2012",

month = jun,

doi = "10.1016/j.jbi.2011.12.007",

language = "English",

volume = "45",

pages = "447--459",

journal = "Journal of Biomedical Informatics",

issn = "1532-0464",

publisher = "Academic Press Inc.",

number = "3",

}

TY - JOUR

T1 - Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models

AU - Garibaldi, Jonathan M.

AU - Zhou, Shang Ming

AU - Wang, Xiao Ying

AU - John, Robert I.

AU - Ellis, Ian O.

PY - 2012/6

Y1 - 2012/6

UR - https://pearl.plymouth.ac.uk/nm-research/724/

U2 - 10.1016/j.jbi.2011.12.007

DO - 10.1016/j.jbi.2011.12.007

M3 - Article

SN - 1532-0464

VL - 45

SP - 447

EP - 459

JO - Journal of Biomedical Informatics

JF - Journal of Biomedical Informatics

IS - 3

ER -

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

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