Differentiating the Personalized Information of the Physician‐Patient Communication for the Chronic Obstructive Pulmonary Disease with General Probabilistic Vector Linguistic Terms

Abstract

To accurately determine the follow-up therapeutic schedules for the chronic obstructive pulmonary disease (COPD) patients, this paper aims to develop the analysis tools for the linguistic evaluation to improve the quality of the physician-patient communication. Firstly, we define the general probabilistic vector linguistic term (GPVLT), which is effective to depict people’s judgements from different sources. Then, we establish the multigranularity linguistic space and discuss the different forms of the probabilistic vector linguistic units (PVLUs) in it. Later on, we propose the nondirectional and the directional potentials of PVLUs, which can grasp the fuzziness and the development direction of the linguistic evaluations, respectively. Last but not least, the cases about the physician-patient communication for COPD and some comparisons with the other related methods are provided to illustrate the effectiveness and practicability of the PVLUs’ potentials.

[Retracted] Emergency Optimization Decision‐Making with Incomplete Probabilistic Information under the Background of COVID‐19

“…Suppose there are m alternatives in total; we must find the most appropriate measure from the m alternatives in the limited time. In general, the decision-making process can be divided into the following steps: firstly, we will obtain a m × m matrix through pairwise comparisons of the m alternatives, and each element of the matrix is indicated by the form of hesitant fuzzy incomplete probabilistic linguistic element [26].…”

“…e missing probability values will be calculated according to equation (26). e value of λ ij,l will be zero if the corresponding value of x ij,l is equal to zero; in contrast, the value of λ ij,l will be one if the corresponding value of x ij,l is not equal to zero.…”

“…It is a complex process to supplement the missing values; it can be divided into three categories according to the total number of items. e first category likes the element h 12 � s 2 (0.4), s 3 (x), s 4 (x) , where the number of items is equal to the number of undesirable events, and so we can obtain the missing probability values directly according to formula (26). e second category likes the element h 13 � s 2 (x), s 4 (x) , where the number of items is less than the number of undesirable events, and so we must decrease the number of undesirable events gradually according to equation ( 23) and obtain the decreased matrix shown as equation (38).…”