A consensus model for Delphi processes with linguistic terms and its application to chronic pain in neonates definition

Abstract

This paper proposes a new model of consensus based on linguistic terms to be implemented in Delphi processes. The model of consensus involves qualitative reasoning techniques and is based on the concept of entropy. The proposed model has the ability to reach consensus automatically without the need for either a moderator or a final interaction among panelists. In addition, it permits panelists to answer with different levels of precision depending on their knowledge on each question. The model defined has been used to establish the relevant features for the definition of a type of chronic disease. A real-case application conducted in the Department of Neonatology of Máxima Medical Center in The Netherlands is presented. This application considers the opinions of stakeholders of neonate healthcare in order to reach a final consensual definition of chronic pain in neonates.

Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective

“…The list of experts who had participated in this survey is given in Table 1. The Delphi method adopted here consisted of three steps (Agell et al , 2015; Wang and Lin, 2008). A list of 28 challenges derived from the literature was provided to all the experts, and similar challenges were removed from the list by these experts.The individual expert opinions, in terms of values, were aggregated anonymously such that the experts did not know about their peers' views; this reduced expert bias.The aggregated opinions in the form of values were communicated to each expert, and they were asked to re-enter the values in consideration of the aggregated views.…”

Influential factors in water planning for sustainable tourism destinations

“…This paper proposes a new integrated method of applying qualitative linguistic terms to the challenge of assessing water resource planning. Based on Agell et al (2015), in which an initial consensus model for Delphi processes with linguistic terms was introduced, we define a new measure of consensus among a set of expert opinions that considers both the linguistic terms used by the experts and their precision. Linguistic modelling used in the proposed method has the following advantages:…”

“…The fuzzy Delphi method introduced by Murray, Pipino, and Gigch (1985) and extended by Kaufmann and Gupta (1988) also uses a fuzzy scale by means of triangle membership functions to achieve consensus among pessimistic, moderate, and optimistic expert assessments in different rounds of the Delphi process. These approaches enable experts to use linguistic labels, but they have a drawback in that they are unable to handle different levels of precision in their assessments, nor achieve a degree of consensus (Agell, Van Ganzewinkel, Sánchez, Roselló, Prats & Andriessen, 2015). To overcome these drawbacks, we apply a linguistic model with absolute order-of-magnitude qualitative labels in the Delphi process.…”

“…Several authors have extended these techniques to encompass reasoning about quantity orders-of-magnitude. Agell et al (2015) offers the use of linguistic terms based on qualitative absolute order-ofmagnitude models in the Delphi process to reach consensus automatically -without a moderator nor a final interaction among panellists.…”

Health Consensus

“…The vote may change because of arguments expressed by participants during a meeting, or as a personal reflection of the participant. Cloud Delphi (Zhang, 2012) Decision Delphi (Rauch, 1979) Dissensus Delphi (Steinert, 2009) Fuzzy Delphi (Murray, Pipino, & van Gigch, 1985) (Agell, van Ganzewinkel, Sánchez, & Roselló, 2015) Time elasticity…”