TY - JOUR
T1 - A comprehensive decision support model for online doctors ranking with interval-valued neutrosophic numbers
AU - Liang, Pei
AU - Hu, Junhua
AU - Chin, KwaiSang
PY - 2024/7
Y1 - 2024/7
N2 - Web-based appointment systems are emerging in the healthcare industry with mass data, but homogenized online information and poor evaluation criteria lead to blindness in selecting doctors. To select an appropriate doctor when making appointments online, a comprehensive decision support model is proposed. First, one class of multi-criteria is built from reviews by text mining technologies. For quantitative analysis, interval-valued neutrosophic numbers (IVNNs) are utilized to describe reviews, and related integration operators of IVNNs are employed. Second, another class of multi-criteria is established by the website-given labels. A disease similarity measure-based transformation method is proposed to redefine the doctors’ specialization, making the evaluation values more discriminable. Finally, a personalized doctor ranking result is derived by integrating the two classes of multi-criteria values with a preference parameter. A case study of Wedoctor.com is conducted to validate the proposed model, and the comparison result indicates that the model can effectively support users’ decision-making.© 2022 The Authors.
AB - Web-based appointment systems are emerging in the healthcare industry with mass data, but homogenized online information and poor evaluation criteria lead to blindness in selecting doctors. To select an appropriate doctor when making appointments online, a comprehensive decision support model is proposed. First, one class of multi-criteria is built from reviews by text mining technologies. For quantitative analysis, interval-valued neutrosophic numbers (IVNNs) are utilized to describe reviews, and related integration operators of IVNNs are employed. Second, another class of multi-criteria is established by the website-given labels. A disease similarity measure-based transformation method is proposed to redefine the doctors’ specialization, making the evaluation values more discriminable. Finally, a personalized doctor ranking result is derived by integrating the two classes of multi-criteria values with a preference parameter. A case study of Wedoctor.com is conducted to validate the proposed model, and the comparison result indicates that the model can effectively support users’ decision-making.© 2022 The Authors.
KW - doctors ranking
KW - homogeneity
KW - interval-valued neutrosophic numbers
KW - multi-criteria
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85137565918&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85137565918&origin=recordpage
U2 - 10.1111/itor.13208
DO - 10.1111/itor.13208
M3 - RGC 21 - Publication in refereed journal
SN - 0969-6016
VL - 31
SP - 2504
EP - 2527
JO - International Transactions in Operational Research
JF - International Transactions in Operational Research
IS - 4
ER -