Extended Reflexive Ontologies for the Generation of Clinical Recommendations

Autores: Eider Sánchez Herrero, Carlos Toro Rodríguez, Manuel Graña, Cesar Sanin, Edward Szczerbicki

Fecha: 17.02.2015

Cybernetics and Systems


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Abstract

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a "reasoning over domain" approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.

BIB_text

@Article {
author = {Eider Sánchez Herrero, Carlos Toro Rodríguez, Manuel Graña, Cesar Sanin, Edward Szczerbicki},
title = {Extended Reflexive Ontologies for the Generation of Clinical Recommendations},
journal = {Cybernetics and Systems},
pages = {4-18},
number = {1},
volume = {46},
keywds = {

fast query system, autopoiesis, reflexive ontologies, rule engine


}
abstract = {

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a "reasoning over domain" approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.


}
isi = {1},
date = {2015-02-17},
year = {2015},
}
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