Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score

Autores
D'negri, Carlos Eduardo; de Vito, Eduardo
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background. Murray score is the result of an equation that gives all its variables the same linear contribution and weight and makes use of consented cut-offs. Everyday physicians' vocabulary is full of terms (adjectives) like: little, small, low, high, etc. that they handle in an intuitive and not always linear way to make therapeutic decisions. The purpose of this paper is to develop a fuzzy logic (FL) vision of Murray's score variables to enable the measurement of physicians' knowledge, experience and intuition in diagnosing lung injury and test if they followed Murray's equation predictions. Methods. For a prospective survey carried out among a team of professionals (aged 29 to 53) in a University Hospital Intensive Care Unit, twelve physicians filled in two questionnaires. In the first one they had to define the ranks which should be categorized as normal, moderate and severe for three of four Murray variables. In another questionnaire, which represented all probable combinations of those categories, they had to tick the pulmonary condition as: no injury, mild, moderate, and ARDS. This procedure gave rise to a Fuzzy Inference System designed to provide the degree of severity as sensed by the group. Results. The survey showed fuzzy frontiers for the categories and fuzzy diagnosis. In all, 45% of the hypothetical patients (n 18,013) were equally diagnosed by the survey and Murray's equation, whereas another 51% was overestimated in one level by the survey. Physicians agreed with 96.5% of ARDS cases according to Murray's test but only 11.6% of its mild cases were equally diagnosed by the survey. Nonlinearity of the survey reasoning (high relevance to gas exchange and chest film) was apparent. Conclusions. The contiguous categories of the variables confirm the existence of fuzzy frontiers. An overestimation was found in the surveyed group's interpretation of severity. This overestimation was mainly due to the different weight assigned to PO2/FiO2and chest film variables. The FL approach made it possible to measure knowledge, experience and intuition as they appear in physicians' thinking. FL methodology could overcome a series of restrictions that current tests have due to cut-offs. © 2010 D'Negri and De Vito; licensee BioMed Central Ltd.
Fil: D'negri, Carlos Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: de Vito, Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina
Materia
ARDS
Fuzzy Logic
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/67632

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spelling Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray scoreD'negri, Carlos Eduardode Vito, EduardoARDSFuzzy Logichttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Background. Murray score is the result of an equation that gives all its variables the same linear contribution and weight and makes use of consented cut-offs. Everyday physicians' vocabulary is full of terms (adjectives) like: little, small, low, high, etc. that they handle in an intuitive and not always linear way to make therapeutic decisions. The purpose of this paper is to develop a fuzzy logic (FL) vision of Murray's score variables to enable the measurement of physicians' knowledge, experience and intuition in diagnosing lung injury and test if they followed Murray's equation predictions. Methods. For a prospective survey carried out among a team of professionals (aged 29 to 53) in a University Hospital Intensive Care Unit, twelve physicians filled in two questionnaires. In the first one they had to define the ranks which should be categorized as normal, moderate and severe for three of four Murray variables. In another questionnaire, which represented all probable combinations of those categories, they had to tick the pulmonary condition as: no injury, mild, moderate, and ARDS. This procedure gave rise to a Fuzzy Inference System designed to provide the degree of severity as sensed by the group. Results. The survey showed fuzzy frontiers for the categories and fuzzy diagnosis. In all, 45% of the hypothetical patients (n 18,013) were equally diagnosed by the survey and Murray's equation, whereas another 51% was overestimated in one level by the survey. Physicians agreed with 96.5% of ARDS cases according to Murray's test but only 11.6% of its mild cases were equally diagnosed by the survey. Nonlinearity of the survey reasoning (high relevance to gas exchange and chest film) was apparent. Conclusions. The contiguous categories of the variables confirm the existence of fuzzy frontiers. An overestimation was found in the surveyed group's interpretation of severity. This overestimation was mainly due to the different weight assigned to PO2/FiO2and chest film variables. The FL approach made it possible to measure knowledge, experience and intuition as they appear in physicians' thinking. FL methodology could overcome a series of restrictions that current tests have due to cut-offs. © 2010 D'Negri and De Vito; licensee BioMed Central Ltd.Fil: D'negri, Carlos Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Vito, Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaBioMed Central2010-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/67632D'negri, Carlos Eduardo; de Vito, Eduardo; Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score; BioMed Central; Bmc Medical Informatics And Decision Making; 10; 1; 11-2010; 1-111472-6947CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-10-70info:eu-repo/semantics/altIdentifier/doi/10.1186/1472-6947-10-70info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:55:35Zoai:ri.conicet.gov.ar:11336/67632instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:55:35.968CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
title Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
spellingShingle Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
D'negri, Carlos Eduardo
ARDS
Fuzzy Logic
title_short Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
title_full Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
title_fullStr Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
title_full_unstemmed Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
title_sort Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score
dc.creator.none.fl_str_mv D'negri, Carlos Eduardo
de Vito, Eduardo
author D'negri, Carlos Eduardo
author_facet D'negri, Carlos Eduardo
de Vito, Eduardo
author_role author
author2 de Vito, Eduardo
author2_role author
dc.subject.none.fl_str_mv ARDS
Fuzzy Logic
topic ARDS
Fuzzy Logic
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Background. Murray score is the result of an equation that gives all its variables the same linear contribution and weight and makes use of consented cut-offs. Everyday physicians' vocabulary is full of terms (adjectives) like: little, small, low, high, etc. that they handle in an intuitive and not always linear way to make therapeutic decisions. The purpose of this paper is to develop a fuzzy logic (FL) vision of Murray's score variables to enable the measurement of physicians' knowledge, experience and intuition in diagnosing lung injury and test if they followed Murray's equation predictions. Methods. For a prospective survey carried out among a team of professionals (aged 29 to 53) in a University Hospital Intensive Care Unit, twelve physicians filled in two questionnaires. In the first one they had to define the ranks which should be categorized as normal, moderate and severe for three of four Murray variables. In another questionnaire, which represented all probable combinations of those categories, they had to tick the pulmonary condition as: no injury, mild, moderate, and ARDS. This procedure gave rise to a Fuzzy Inference System designed to provide the degree of severity as sensed by the group. Results. The survey showed fuzzy frontiers for the categories and fuzzy diagnosis. In all, 45% of the hypothetical patients (n 18,013) were equally diagnosed by the survey and Murray's equation, whereas another 51% was overestimated in one level by the survey. Physicians agreed with 96.5% of ARDS cases according to Murray's test but only 11.6% of its mild cases were equally diagnosed by the survey. Nonlinearity of the survey reasoning (high relevance to gas exchange and chest film) was apparent. Conclusions. The contiguous categories of the variables confirm the existence of fuzzy frontiers. An overestimation was found in the surveyed group's interpretation of severity. This overestimation was mainly due to the different weight assigned to PO2/FiO2and chest film variables. The FL approach made it possible to measure knowledge, experience and intuition as they appear in physicians' thinking. FL methodology could overcome a series of restrictions that current tests have due to cut-offs. © 2010 D'Negri and De Vito; licensee BioMed Central Ltd.
Fil: D'negri, Carlos Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: de Vito, Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina
description Background. Murray score is the result of an equation that gives all its variables the same linear contribution and weight and makes use of consented cut-offs. Everyday physicians' vocabulary is full of terms (adjectives) like: little, small, low, high, etc. that they handle in an intuitive and not always linear way to make therapeutic decisions. The purpose of this paper is to develop a fuzzy logic (FL) vision of Murray's score variables to enable the measurement of physicians' knowledge, experience and intuition in diagnosing lung injury and test if they followed Murray's equation predictions. Methods. For a prospective survey carried out among a team of professionals (aged 29 to 53) in a University Hospital Intensive Care Unit, twelve physicians filled in two questionnaires. In the first one they had to define the ranks which should be categorized as normal, moderate and severe for three of four Murray variables. In another questionnaire, which represented all probable combinations of those categories, they had to tick the pulmonary condition as: no injury, mild, moderate, and ARDS. This procedure gave rise to a Fuzzy Inference System designed to provide the degree of severity as sensed by the group. Results. The survey showed fuzzy frontiers for the categories and fuzzy diagnosis. In all, 45% of the hypothetical patients (n 18,013) were equally diagnosed by the survey and Murray's equation, whereas another 51% was overestimated in one level by the survey. Physicians agreed with 96.5% of ARDS cases according to Murray's test but only 11.6% of its mild cases were equally diagnosed by the survey. Nonlinearity of the survey reasoning (high relevance to gas exchange and chest film) was apparent. Conclusions. The contiguous categories of the variables confirm the existence of fuzzy frontiers. An overestimation was found in the surveyed group's interpretation of severity. This overestimation was mainly due to the different weight assigned to PO2/FiO2and chest film variables. The FL approach made it possible to measure knowledge, experience and intuition as they appear in physicians' thinking. FL methodology could overcome a series of restrictions that current tests have due to cut-offs. © 2010 D'Negri and De Vito; licensee BioMed Central Ltd.
publishDate 2010
dc.date.none.fl_str_mv 2010-11
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info:eu-repo/semantics/publishedVersion
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format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/67632
D'negri, Carlos Eduardo; de Vito, Eduardo; Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score; BioMed Central; Bmc Medical Informatics And Decision Making; 10; 1; 11-2010; 1-11
1472-6947
CONICET Digital
CONICET
url http://hdl.handle.net/11336/67632
identifier_str_mv D'negri, Carlos Eduardo; de Vito, Eduardo; Making it possible to measure knowledge, experience and intuition in diagnosing lung injury severity: A fuzzy logic vision based on the Murray score; BioMed Central; Bmc Medical Informatics And Decision Making; 10; 1; 11-2010; 1-11
1472-6947
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/doi/10.1186/1472-6947-10-70
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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