CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability

Detalhes bibliográficos
Autor(a) principal: Barbosa, George C. G.
Data de Publicação: 2020
Outros Autores: Ali, M. Sanni, Araujo, Bruno, Reis, Sandra, Sena, Samila, Ichihara, Maria Y. T., Pescarini, Julia, Fiaccone, Rosemeire L., Amorim, Leila D., Pita, Robespierre, Barreto, Marcos E., Smeeth, Liam, Barreto, Mauricio Lima
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/45893
Resumo: CIDACS and the 100 Million Cohort has support from the Health Surveillance Secretariat (Ministry of Health, Brazil), National Research Council (CNPq), Brazilian Funding Agency for Science, Technology and Innovation (FINEP), Bahia State Research Agency (FAPESB), Bahia State Secretariat of Science and Technology (SECTI), Bill and Melinda Gates Foundation, and Wellcome Trust (UK). The funding sources have no role in the design of the study, its execution, analysis and interpretation, manuscript writing, and dissemination of results.
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spelling Barbosa, George C. G.Ali, M. SanniAraujo, BrunoReis, SandraSena, SamilaIchihara, Maria Y. T.Pescarini, JuliaFiaccone, Rosemeire L.Amorim, Leila D.Pita, RobespierreBarreto, Marcos E.Smeeth, LiamBarreto, Mauricio Lima2021-02-01T13:38:10Z2021-02-01T13:38:10Z2020BARBOSA, George C. G. et al. CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability. BMC Medical Informatics and Decision Making, v. 20, p. 289, 2020.1472-6947https://www.arca.fiocruz.br/handle/icict/4589310.1186/s12911-020-01285-wCIDACS and the 100 Million Cohort has support from the Health Surveillance Secretariat (Ministry of Health, Brazil), National Research Council (CNPq), Brazilian Funding Agency for Science, Technology and Innovation (FINEP), Bahia State Research Agency (FAPESB), Bahia State Secretariat of Science and Technology (SECTI), Bill and Melinda Gates Foundation, and Wellcome Trust (UK). The funding sources have no role in the design of the study, its execution, analysis and interpretation, manuscript writing, and dissemination of results.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / London School of Hygiene and Tropical Medicine. Department of Non‑communicable Disease Epidemiology. London, UK / University of Oxford. Center for Statistics in Medicine. Oxford, UK.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Federal University of Bahia. Department of Statistics. Salvador, BA, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Federal University of Bahia. Department of Statistics. Salvador, BA, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Federal University of Bahia. Computer Science Department. Salvador, BA, Brazil / London School of Economics and Political Science. Department of Statistics. London, UK.London School of Hygiene and Tropical Medicine. Department of Non‑communicable Disease Epidemiology. London, UK / University of Oxford. Center for Statistics in Medicine. Oxford, UK.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimento para Saúde. Salvador, BA, Brasil / Federal University of Bahia. Institute of Public Health. Salvador, BA, Brazil.Record linkage is the process of identifying and combining records about the same individual from two or more different datasets. While there are many open source and commercial data linkage tools, the volume and complexity of currently available datasets for linkage pose a huge challenge; hence, designing an efficient linkage tool with reasonable accuracy and scalability is required. Methods: We developed CIDACS-RL (Centre for Data and Knowledge Integration for Health – Record Linkage), a novel iterative deterministic record linkage algorithm based on a combination of indexing search and scoring algorithms (provided by Apache Lucene). We described how the algorithm works and compared its performance with four open source linkage tools (AtyImo, Febrl, FRIL and RecLink) in terms of sensitivity and positive predictive value using gold standard dataset. We also evaluated its accuracy and scalability using a case-study and its scalability and execution time using a simulated cohort in serial (single core) and multi-core (eight core) computation settings. Results: Overall, CIDACS-RL algorithm had a superior performance: positive predictive value (99.93% versus AtyImo 99.30%, RecLink 99.5%, Febrl 98.86%, and FRIL 96.17%) and sensitivity (99.87% versus AtyImo 98.91%, RecLink 73.75%, Febrl 90.58%, and FRIL 74.66%). In the case study, using a ROC curve to choose the most appropriate cut-off value (0.896), the obtained metrics were: sensitivity = 92.5% (95% CI 92.07–92.99), specificity = 93.5% (95% CI 93.08–93.8) and area under the curve (AUC) = 97% (95% CI 96.97–97.35). The multi-core computation was about four times faster (150 seconds) than the serial setting (550 seconds) when using a dataset of 20 million records. Conclusion: CIDACS-RL algorithm is an innovative linkage tool for huge datasets, with higher accuracy, improved scalability, and substantially shorter execution time compared to other existing linkage tools. In addition, CIDACS-RL can be deployed on standard computers without the need for high-speed processors and distributed infrastructures.engBMCPrecisãoLigação de dadosIndexaçãoRecuperação de informaçãoPesquisaAccuracyData linkageEntity resolutionIndexingInformation retrieval techniquesScalabilityScoring SearchCIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalabilityinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-83097https://www.arca.fiocruz.br/bitstream/icict/45893/1/license.txt36b51ef91c52b5338d9d29ba0cc807bcMD51ORIGINALBARBOSA et al CIDACS-RL a novel indexing...2020 v.20 n.289.pdfBARBOSA et al CIDACS-RL a novel indexing...2020 v.20 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dc.title.pt_BR.fl_str_mv CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
title CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
spellingShingle CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
Barbosa, George C. G.
Precisão
Ligação de dados
Indexação
Recuperação de informação
Pesquisa
Accuracy
Data linkage
Entity resolution
Indexing
Information retrieval techniques
Scalability
Scoring Search
title_short CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
title_full CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
title_fullStr CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
title_full_unstemmed CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
title_sort CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
author Barbosa, George C. G.
author_facet Barbosa, George C. G.
Ali, M. Sanni
Araujo, Bruno
Reis, Sandra
Sena, Samila
Ichihara, Maria Y. T.
Pescarini, Julia
Fiaccone, Rosemeire L.
Amorim, Leila D.
Pita, Robespierre
Barreto, Marcos E.
Smeeth, Liam
Barreto, Mauricio Lima
author_role author
author2 Ali, M. Sanni
Araujo, Bruno
Reis, Sandra
Sena, Samila
Ichihara, Maria Y. T.
Pescarini, Julia
Fiaccone, Rosemeire L.
Amorim, Leila D.
Pita, Robespierre
Barreto, Marcos E.
Smeeth, Liam
Barreto, Mauricio Lima
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Barbosa, George C. G.
Ali, M. Sanni
Araujo, Bruno
Reis, Sandra
Sena, Samila
Ichihara, Maria Y. T.
Pescarini, Julia
Fiaccone, Rosemeire L.
Amorim, Leila D.
Pita, Robespierre
Barreto, Marcos E.
Smeeth, Liam
Barreto, Mauricio Lima
dc.subject.other.pt_BR.fl_str_mv Precisão
Ligação de dados
Indexação
Recuperação de informação
Pesquisa
topic Precisão
Ligação de dados
Indexação
Recuperação de informação
Pesquisa
Accuracy
Data linkage
Entity resolution
Indexing
Information retrieval techniques
Scalability
Scoring Search
dc.subject.en.pt_BR.fl_str_mv Accuracy
Data linkage
Entity resolution
Indexing
Information retrieval techniques
Scalability
Scoring Search
description CIDACS and the 100 Million Cohort has support from the Health Surveillance Secretariat (Ministry of Health, Brazil), National Research Council (CNPq), Brazilian Funding Agency for Science, Technology and Innovation (FINEP), Bahia State Research Agency (FAPESB), Bahia State Secretariat of Science and Technology (SECTI), Bill and Melinda Gates Foundation, and Wellcome Trust (UK). The funding sources have no role in the design of the study, its execution, analysis and interpretation, manuscript writing, and dissemination of results.
publishDate 2020
dc.date.issued.fl_str_mv 2020
dc.date.accessioned.fl_str_mv 2021-02-01T13:38:10Z
dc.date.available.fl_str_mv 2021-02-01T13:38:10Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv BARBOSA, George C. G. et al. CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability. BMC Medical Informatics and Decision Making, v. 20, p. 289, 2020.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/45893
dc.identifier.issn.pt_BR.fl_str_mv 1472-6947
dc.identifier.doi.none.fl_str_mv 10.1186/s12911-020-01285-w
identifier_str_mv BARBOSA, George C. G. et al. CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability. BMC Medical Informatics and Decision Making, v. 20, p. 289, 2020.
1472-6947
10.1186/s12911-020-01285-w
url https://www.arca.fiocruz.br/handle/icict/45893
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
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