Intelligent decision support system for precision medicine: time series multi-variable approach for data processing
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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 |
_version_ |
1817544566967894016 |