Data-driven group decision making for diagnosis of thyroid nodule  

作　　者：Chao FU Wenjun CHANG Weiyong LIU Shanlin YANG 

机构地区：[1]School of Management,Hefei University of Technology,Hefei 230009,China [2]Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China [3]Department of Ultrasound,The First Affiliated Hospital of USTC,Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei 230001,China

出　　处：《Science China(Information Sciences)》2019年第11期147-169,共23页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.71622003,71571060,71690235,71690230,71521001)

摘　　要：Emerging information technologies’ integration into various fields has enhanced the development of these fields. Large volumes of data have been accumulated in this process. The accumulated data offer opportunities and challenges for people facing practical problems. On the one hand, it is essential to depend on a group’s capabilities rather than an individual’s capabilities to handle practical problems because the individual may lack sufficient expertise and experience to use data. In this situation, the practical problems can be considered as group decision making(GDM) problems. On the other hand, the accumulated data can help generate quality solutions to GDM problems. To obtain such solutions under the assumption that the accumulated data regarding a specific decision problem are available, this paper proposes a data-driven GDM method. In the method, decision makers’ weights are learned from historical overall assessments and the corresponding gold standards, while criterion weights are learned from historical overall assessments and the corresponding decision matrices. The learned expert weights and criterion weights are used to produce the aggregated assessments, from which alternatives are compared or the overall conclusion is made. In a tertiary hospital located in Hefei, Anhui Province, China, the proposed method is applied to aid radiologists in diagnosing thyroid nodules.Emerging information technologies’ integration into various fields has enhanced the development of these fields. Large volumes of data have been accumulated in this process. The accumulated data offer opportunities and challenges for people facing practical problems. On the one hand, it is essential to depend on a group’s capabilities rather than an individual’s capabilities to handle practical problems because the individual may lack sufficient expertise and experience to use data. In this situation, the practical problems can be considered as group decision making(GDM) problems. On the other hand, the accumulated data can help generate quality solutions to GDM problems. To obtain such solutions under the assumption that the accumulated data regarding a specific decision problem are available, this paper proposes a data-driven GDM method. In the method, decision makers’ weights are learned from historical overall assessments and the corresponding gold standards, while criterion weights are learned from historical overall assessments and the corresponding decision matrices. The learned expert weights and criterion weights are used to produce the aggregated assessments, from which alternatives are compared or the overall conclusion is made. In a tertiary hospital located in Hefei, Anhui Province, China, the proposed method is applied to aid radiologists in diagnosing thyroid nodules.

关 键 词：DATA-DRIVEN group decision making interval number LEARNING of expert WEIGHTS LEARNING of criterion WEIGHTS DIAGNOSIS of THYROID NODULE 

分 类 号：TP[自动化与计算机技术]