TY - JOUR
T1 - Multi-criteria appraisal recommendation
AU - Fu, Chao
AU - Zhan, Qianshan
AU - Chang, Leilei
AU - Liu, Weiyong
AU - Yang, Shanlin
N1 - Publisher Copyright:
© Operational Research Society 2022.
PY - 2023
Y1 - 2023
N2 - Generating the overall assessments of cases from their observations on multiple criteria when large volumes of historical data have been accumulated is a key issue. This study, therefore, developed the framework of multi-criteria appraisal recommendation (MCAR). Five strategies belonging to three categories were designed to recommend the overall appraisals of new cases from their observations on multiple criteria based on relevant historical data. The proposed framework’s basic conditions and key issues were presented to widen its application. The framework was then used to generate the diagnostic recommendations for thyroid nodules from their observations based on the historical examination reports of six radiologists. The experimental results indicated that different strategies are appropriate for different radiologists, and no single strategy was found to be the most appropriate for all considered radiologists. The five strategies were compared with four representative machine learning models to highlight their performances and interpretabilities using the historical examination reports of the radiologists.
AB - Generating the overall assessments of cases from their observations on multiple criteria when large volumes of historical data have been accumulated is a key issue. This study, therefore, developed the framework of multi-criteria appraisal recommendation (MCAR). Five strategies belonging to three categories were designed to recommend the overall appraisals of new cases from their observations on multiple criteria based on relevant historical data. The proposed framework’s basic conditions and key issues were presented to widen its application. The framework was then used to generate the diagnostic recommendations for thyroid nodules from their observations based on the historical examination reports of six radiologists. The experimental results indicated that different strategies are appropriate for different radiologists, and no single strategy was found to be the most appropriate for all considered radiologists. The five strategies were compared with four representative machine learning models to highlight their performances and interpretabilities using the historical examination reports of the radiologists.
KW - case similarity
KW - criterion aggregation
KW - diagnosis of thyroid nodules
KW - Multi-criteria analysis
KW - observation transformation
KW - selection of recommendation strategies
UR - https://www.scopus.com/pages/publications/85122861113
U2 - 10.1080/01605682.2021.2023674
DO - 10.1080/01605682.2021.2023674
M3 - 文章
AN - SCOPUS:85122861113
SN - 0160-5682
VL - 74
SP - 81
EP - 92
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
IS - 1
ER -