CIDACS‑RL: a novel indexing search and scoring‑based record linkage system for huge datasets with high accuracy and scalability
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , , , , , |
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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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 |
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https://www.arca.fiocruz.br/handle/icict/45893 |
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eng |
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eng |
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BMC |
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BMC |
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