Decisional DNA for modeling and reuse of experiential clinical assessments in breast cancer diagnosis and treatment
Autores: Eider Sanchez and Wang Peng and Carlos Toro and Cesar Sanin and Manuel Graña and Edward Szczerbicki and Eduardo Carrasco and Frank Guijarro and Luis Brualla
Fecha: 25.12.2014
Neurocomputing
Abstract
Abstract Clinical Decision Support Systems (CDSS) are active knowledge resources that use patient data to generate case specific advice. The fast pace of change of clinical knowledge imposes to {CDSS} the continuous update of the domain knowledge and decision criteria. Traditional approaches require costly tedious manual maintenance of the {CDSS} knowledge bases and repositories. Often, such an effort cannot be assumed by medical teams, hence maintenance is often faulty. In this paper, we propose a (semi-)automatic update process of the underlying knowledge bases and decision criteria of CDSS, following a learning paradigm based on previous experiences, such as the continuous learning that clinicians carry out in real life. In this process clinical decisional events are acquired and formalized inside the system by the use of the {SOEKS} and Decisional {DNA} experiential knowledge representation techniques. We propose three algorithms processing clinical experience to: (a) provide a weighting of the different decision criteria, (b) obtain their fine-tuning, and (c) achieve the formalization of new decision criteria. Finally, we present an implementation instance of a {CDSS} for the domain of breast cancer diagnosis and treatment.
BIB_text
author = {Eider Sanchez and Wang Peng and Carlos Toro and Cesar Sanin and Manuel Graña and Edward Szczerbicki and Eduardo Carrasco and Frank Guijarro and Luis Brualla},
title = {Decisional DNA for modeling and reuse of experiential clinical assessments in breast cancer diagnosis and treatment},
journal = {Neurocomputing },
pages = {308-318},
volume = {146},
keywds = {
Decisional DNA, Set of experience knowledge structure, clinical decision support systems, semantic technologies, breast cancer diagnosis and treatment
}
abstract = {
Abstract Clinical Decision Support Systems (CDSS) are active knowledge resources that use patient data to generate case specific advice. The fast pace of change of clinical knowledge imposes to {CDSS} the continuous update of the domain knowledge and decision criteria. Traditional approaches require costly tedious manual maintenance of the {CDSS} knowledge bases and repositories. Often, such an effort cannot be assumed by medical teams, hence maintenance is often faulty. In this paper, we propose a (semi-)automatic update process of the underlying knowledge bases and decision criteria of CDSS, following a learning paradigm based on previous experiences, such as the continuous learning that clinicians carry out in real life. In this process clinical decisional events are acquired and formalized inside the system by the use of the {SOEKS} and Decisional {DNA} experiential knowledge representation techniques. We propose three algorithms processing clinical experience to: (a) provide a weighting of the different decision criteria, (b) obtain their fine-tuning, and (c) achieve the formalization of new decision criteria. Finally, we present an implementation instance of a {CDSS} for the domain of breast cancer diagnosis and treatment.
}
isi = {1},
date = {2014-12-25},
year = {2014},
}
