Intelligent decision support system for precision medicine: time series multi-variable approach for data processing

Detalhes bibliográficos
Autor(a) principal: Mosavi, Nasimsadat
Data de Publicação: 2022
Outros Autores: Santos, Manuel
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/90403
Resumo: This study has introduced a new approach to clinical data processing. Clinical data is unstructured, heterogeneous, and comes from various resources. Although, the challenges associated with processing such data have been discussed widely in literature, addressing those aspects is fragmented and case-based. This paper presents the initial outcome of applying the Time series Multi-Variables model (TsMV) to 12 different datasets from Intensive Care Units (ICU), medications, and laboratories. TsMV supports the development of an Intelligent Decision Support System for PM (IDSS4PM) by preparing effective data. Moreover, the CRISP-DM methodology was employed, and based on the proposed solution, we have adjusted the significant steps to CRISP-DM, where those extra phases are essential for taking future works.
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spelling Intelligent decision support system for precision medicine: time series multi-variable approach for data processingData ProcessingIntelligent Decision SupportIntensive Care UnitOptimizationPrecision MedicineEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaThis study has introduced a new approach to clinical data processing. Clinical data is unstructured, heterogeneous, and comes from various resources. Although, the challenges associated with processing such data have been discussed widely in literature, addressing those aspects is fragmented and case-based. This paper presents the initial outcome of applying the Time series Multi-Variables model (TsMV) to 12 different datasets from Intensive Care Units (ICU), medications, and laboratories. TsMV supports the development of an Intelligent Decision Support System for PM (IDSS4PM) by preparing effective data. Moreover, the CRISP-DM methodology was employed, and based on the proposed solution, we have adjusted the significant steps to CRISP-DM, where those extra phases are essential for taking future works.FCT - Fundação para a Ciência e a Tecnologia (DSAIPA/DS/0084/2018)SCITEPRESSUniversidade do MinhoMosavi, NasimsadatSantos, Manuel20222022-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/1822/90403engMosavi, N.S., & Santos, M.F. (2022). Intelligent Decision Support System for Precision Medicine; Time Series Multi-variable Approach for Data Processing. International Conference on Knowledge Discovery and Information Retrieval.9789897586149info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-11T05:16:11Zoai:repositorium.sdum.uminho.pt:1822/90403Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T05:16:11Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
title Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
spellingShingle Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
Mosavi, Nasimsadat
Data Processing
Intelligent Decision Support
Intensive Care Unit
Optimization
Precision Medicine
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
title_short Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
title_full Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
title_fullStr Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
title_full_unstemmed Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
title_sort Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
author Mosavi, Nasimsadat
author_facet Mosavi, Nasimsadat
Santos, Manuel
author_role author
author2 Santos, Manuel
author2_role author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Mosavi, Nasimsadat
Santos, Manuel
dc.subject.por.fl_str_mv Data Processing
Intelligent Decision Support
Intensive Care Unit
Optimization
Precision Medicine
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
topic Data Processing
Intelligent Decision Support
Intensive Care Unit
Optimization
Precision Medicine
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
description This study has introduced a new approach to clinical data processing. Clinical data is unstructured, heterogeneous, and comes from various resources. Although, the challenges associated with processing such data have been discussed widely in literature, addressing those aspects is fragmented and case-based. This paper presents the initial outcome of applying the Time series Multi-Variables model (TsMV) to 12 different datasets from Intensive Care Units (ICU), medications, and laboratories. TsMV supports the development of an Intelligent Decision Support System for PM (IDSS4PM) by preparing effective data. Moreover, the CRISP-DM methodology was employed, and based on the proposed solution, we have adjusted the significant steps to CRISP-DM, where those extra phases are essential for taking future works.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/90403
url https://hdl.handle.net/1822/90403
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Mosavi, N.S., & Santos, M.F. (2022). Intelligent Decision Support System for Precision Medicine; Time Series Multi-variable Approach for Data Processing. International Conference on Knowledge Discovery and Information Retrieval.
9789897586149
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv SCITEPRESS
publisher.none.fl_str_mv SCITEPRESS
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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