Proceso de consenso en modelos mentales y aplicación al desarrollo de software ágil en bioinformática / Consensus process in mental models and its application to agile software development in bioinformatics

Los mapas cognitivos difusos han resultado útiles para la representación de modelos mentales tanto individuales como colectivos. Para problemas tales como el análisis de sistemas complejos y la toma de decisiones usualmente resulta útil realizar un proceso de consenso que permita lograr en el grupo un estado de acuerdo mutuo entre sus miembros. En el presente trabajo se desarrolla un modelo para procesos de consenso en modelos mentales con el uso de mapas cognitivos difusos y de la computación con palabras mediante el modelo de representación lingüística basada en 2-tuplas. El modelo se presenta de forma gráfica y se describen sus principales actividades. Se presenta un caso de estudio aplicado al desarrollo de software para la bioinformática...

Fuzzy cognitive maps have proven useful to represent both individual and group mental models. When dealing with problems such as the analysis of complex systems or decision making, it is usually advisable to perform a consensus process allowing to achieve mutual agreement between the members of the team. In this paper a model is developed for consensus processes in mental models with the use of fuzzy cognitive maps and computing with words, based on the 2-tuple linguistic representation model. The model is shown graphically and a description is provided of its main activities. A study case is presented which has to do with software development for bioinformatics...

Design of Activation Functions for Inference of Fuzzy Cognitive Maps: Application to Clinical Decision Making in Diagnosis of Pulmonary Infection / 대한의료정보학회지

OBJECTIVES: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. METHODS: The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. RESULTS: Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. CONCLUSIONS: Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.

Design of Activation Functions for Inference of Fuzzy Cognitive Maps: Application to Clinical Decision Making in Diagnosis of Pulmonary Infection / 대한의료정보학회지

OBJECTIVES: Fuzzy cognitive maps (FCMs) representing causal knowledge of relationships between medical concepts have been used as prediction tools for clinical decision making. Activation functions used for inferences of FCMs are very important factors in helping physicians make correct decision. Therefore, in order to increase the visibility of inference results, we propose a method for designing certain types of activation functions by considering the characteristics of FCMs. METHODS: The activation functions, such as the sinusoidal-type function and linear function, are designed by calculating the domain range of the functions to be reached during the inference process of FCMs. Moreover, the designed activation functions were applied to the decision making process with the inference of an FCM model representing the causal knowledge of pulmonary infections. RESULTS: Even though sinusoidal-type functions oscillate and linear functions monotonously increase within the entire range of the domain, the designed activation functions make the inference stable because the proposed method notices where the function is used in the inference. And, the designed functions provide more visible numeric results than do other functions. CONCLUSIONS: Comparing inference results derived using activation functions designed with the proposed method and results derived using activation functions designed with the existing method, we confirmed that the proposed method could be more appropriately used for designing activation functions for the inference process of an FCM for clinical decision making.