IR@PKUHSC  > 北京大学临床肿瘤学院
学科主题临床医学
A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer
Zhou, Zhi-Guo1,2; Liu, Fang1,2; Jiao, Li-Cheng2; Zhou, Zhi-Jie3; Yang, Jian-Bo4; Gong, Mao-Guo2; Zhang, Xiao-Peng5
关键词Clinical Decision Support System Gastric Cancer Lymph Node Metastasis Belief Rule Base Clonal Selection Algorithm
刊名KNOWLEDGE-BASED SYSTEMS
2013-12-01
DOI10.1016/j.knosys.2013.09.001
54期:SI页:128-136
收录类别SCI
文章类型Article
WOS标题词Science & Technology
类目[WOS]Computer Science, Artificial Intelligence
研究领域[WOS]Computer Science
关键词[WOS]EVIDENTIAL REASONING APPROACH ; PIPELINE LEAK DETECTION ; EXPERT-SYSTEM ; CHEST-PAIN ; UNCERTAINTY ; ALGORITHM ; OPTIMIZATION ; INFERENCE ; METHODOLOGY ; PROGNOSIS
英文摘要

Lymph Node Metastasis (LNM) in gastric cancer is an important prognostic factor regarding long-term survival. As it is difficult for doctors to combine multiple factors for a comprehensive analysis, Clinical Decision Support System (CDSS) is desired to help the analysis. In this paper, a novel Bi-level Belief Rule Based (BBRB) prototype CDSS is proposed. The CDSS consists of a two-layer Belief Rule Base (BRB) system. It can be used to handle uncertainty in both clinical data and specific domain knowledge. Initial BRBs are constructed by domain specific knowledge, which may not be accurate. Traditional methods for optimizing BRB are sensitive to initialization and are limited by their weak local searching abilities. In this paper, a new Clonal Selection Algorithm (CSA) is proposed to train a BRB system. Based on CSA, efficient global search can be achieved by reproducing individuals and selecting their improved maturated progenies after the affinity maturation process. The proposed prototype CDSS is validated using a set of real patient data and performs extremely well. In particular, BBRB is capable of providing more reliable and informative diagnosis than a single-layer BRB system in the case study. Compared with conventional optimization method, the new CSA could improve the diagnostic performance further by trying to avoid immature convergence to local optima. (C) 2013 Elsevier B.V. All rights reserved.

语种英语
WOS记录号WOS:000327685800012
引用统计
被引频次:15[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/51585
专题北京大学临床肿瘤学院
作者单位1.High Tech Inst Xian, Xian 710025, Shaanxi, Peoples R China
2.Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China
3.Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China
4.Univ Manchester, Manchester Business Sch, Manchester M15 6PB, Lancs, England
5.Peking Univ, Sch Oncol, Beijing Canc Hosp & Inst,Dept Radiol, Key Lab Carcinogenesis & Translat Res,Minist Educ, Beijing 100142, Peoples R China
推荐引用方式
GB/T 7714
Zhou, Zhi-Guo,Liu, Fang,Jiao, Li-Cheng,et al. A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer[J]. KNOWLEDGE-BASED SYSTEMS,2013,54(SI):128-136.
APA Zhou, Zhi-Guo.,Liu, Fang.,Jiao, Li-Cheng.,Zhou, Zhi-Jie.,Yang, Jian-Bo.,...&Zhang, Xiao-Peng.(2013).A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer.KNOWLEDGE-BASED SYSTEMS,54(SI),128-136.
MLA Zhou, Zhi-Guo,et al."A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer".KNOWLEDGE-BASED SYSTEMS 54.SI(2013):128-136.
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