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Rule-based system for effective clinical decision support

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Resumo:Clinical Decision Support Systems (CDSS) are being increasingly requested and are an important role in health units. Due to the high number of data produced daily, it is necessary that these data are stored and manipulated in order to acquire knowledge to assist the decision-making processes. Representing knowledge in knowledge-based systems is one of the main tasks for achieving an effective CDSS. In this way, this narrative literature review article intends to identify different approaches to represent knowledge for rule-based CDSS. Four models are described, namely decision tables, decision trees, bayesian network and nearest neighbors, emphasizing the first two.
Autores principais:Silva, Beatriz
Outros Autores:Hak, Francini; Guimarães, Tiago; Manuel, Maria; Santos, Manuel Filipe
Assunto:Clinical decision support Rule-based systems Knowledge representation Decision table Decision tree
Ano:2023
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
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author Silva, Beatriz
author2 Hak, Francini
Guimarães, Tiago
Manuel, Maria
Santos, Manuel Filipe
author2_role author
author
author
author
author_facet Silva, Beatriz
Hak, Francini
Guimarães, Tiago
Manuel, Maria
Santos, Manuel Filipe
author_role author
contributor_name_str_mv Universidade do Minho
country_str PT
creators_json_txt [{\"Person.name\":\"Silva, Beatriz\"},{\"Person.name\":\"Hak, Francini\"},{\"Person.name\":\"Guimarães, Tiago\"},{\"Person.name\":\"Manuel, Maria\"},{\"Person.name\":\"Santos, Manuel Filipe\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Universidade do Minho
datacite.creators.creator.creatorName.fl_str_mv Silva, Beatriz
Hak, Francini
Guimarães, Tiago
Manuel, Maria
Santos, Manuel Filipe
datacite.date.Accepted.fl_str_mv 2023-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2024-03-14T14:23:03Z
datacite.date.embargoed.fl_str_mv 2024-03-14T14:23:03Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Clinical decision support
Rule-based systems
Knowledge representation
Decision table
Decision tree
datacite.titles.title.fl_str_mv Rule-based system for effective clinical decision support
dc.contributor.none.fl_str_mv Universidade do Minho
dc.creator.none.fl_str_mv Silva, Beatriz
Hak, Francini
Guimarães, Tiago
Manuel, Maria
Santos, Manuel Filipe
dc.date.Accepted.fl_str_mv 2023-01-01T00:00:00Z
dc.date.available.fl_str_mv 2024-03-14T14:23:03Z
dc.date.embargoed.fl_str_mv 2024-03-14T14:23:03Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://hdl.handle.net/1822/89535
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv Elsevier
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv Clinical decision support
Rule-based systems
Knowledge representation
Decision table
Decision tree
dc.title.fl_str_mv Rule-based system for effective clinical decision support
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_5794
description Clinical Decision Support Systems (CDSS) are being increasingly requested and are an important role in health units. Due to the high number of data produced daily, it is necessary that these data are stored and manipulated in order to acquire knowledge to assist the decision-making processes. Representing knowledge in knowledge-based systems is one of the main tasks for achieving an effective CDSS. In this way, this narrative literature review article intends to identify different approaches to represent knowledge for rule-based CDSS. Four models are described, namely decision tables, decision trees, bayesian network and nearest neighbors, emphasizing the first two.
dirty 0
eu_rights_str_mv openAccess
format conferencePaper
fulltext.url.fl_str_mv https://prod-dspace.uminho.pt/bitstreams/759dae17-d0f9-4c7f-a356-39fa629d6bbe/download
id rum_7ec63d0f0e7d8ab000ee2fcfe46f7bf1
identifier.url.fl_str_mv https://hdl.handle.net/1822/89535
instacron_str repositorium
institution Universidade do Minho
instname_str Universidade do Minho
language eng
network_acronym_str rum
network_name_str RepositóriUM - Universidade do Minho
oai_identifier_str oai:repositorium.uminho.pt:1822/89535
organization_str_mv urn:organizationAcronym:repositorium
person_str_mv Silva, Beatriz
Hak, Francini
Guimarães, Tiago
Manuel, Maria
Santos, Manuel Filipe
publishDate 2023
publisher.none.fl_str_mv Elsevier
reponame_str RepositóriUM - Universidade do Minho
repository_id_str urn:repositoryAcronym:rum
service_str_mv urn:repositoryAcronym:rum
spelling engElsevierporClinical Decision Support Systems (CDSS) are being increasingly requested and are an important role in health units. Due to the high number of data produced daily, it is necessary that these data are stored and manipulated in order to acquire knowledge to assist the decision-making processes. Representing knowledge in knowledge-based systems is one of the main tasks for achieving an effective CDSS. In this way, this narrative literature review article intends to identify different approaches to represent knowledge for rule-based CDSS. Four models are described, namely decision tables, decision trees, bayesian network and nearest neighbors, emphasizing the first two.application/pdfporRule-based system for effective clinical decision supportSilva, BeatrizHak, FranciniGuimarães, TiagoManuel, MariaSantos, Manuel FilipeHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptDOIIsPartOf10.1016/j.procs.2023.03.1192024-03-14T14:23:03Z20232023-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/89535http://purl.org/coar/access_right/c_abf2open accessClinical decision supportRule-based systemsKnowledge representationDecision tableDecision tree502534 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/759dae17-d0f9-4c7f-a356-39fa629d6bbe/download
spellingShingle Rule-based system for effective clinical decision support
Silva, Beatriz
Clinical decision support
Rule-based systems
Knowledge representation
Decision table
Decision tree
status SINGLETON
subject.fl_str_mv Clinical decision support
Rule-based systems
Knowledge representation
Decision table
Decision tree
title Rule-based system for effective clinical decision support
title_full Rule-based system for effective clinical decision support
title_fullStr Rule-based system for effective clinical decision support
title_full_unstemmed Rule-based system for effective clinical decision support
title_short Rule-based system for effective clinical decision support
title_sort Rule-based system for effective clinical decision support
topic Clinical decision support
Rule-based systems
Knowledge representation
Decision table
Decision tree
topic_facet Clinical decision support
Rule-based systems
Knowledge representation
Decision table
Decision tree
url https://hdl.handle.net/1822/89535
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