Evidence map›Paper›PMID 33167998›Full record

ArticleBMC medical informatics and decision making2020

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

George C G Barbosa, M Sanni Ali, Bruno Araujo, Sandra Reis, Samila Sena, Maria Y T Ichihara, Julia Pescarini, Rosemeire L Fiaccone, Leila D Amorim, Robespierre Pita and 3 more

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

60 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Record linkage in tuberculosis epidemiological surveillance in Brazil: systematic methodological literature review.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2026
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  18. Association between congenital Zika syndrome and hospitalizations during early childhood: a nationwide cohort study.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

George C G BarbosaCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil. gcgbarbosa@gmail.com.ORCID 0000-0003-3036-9899
M Sanni AliCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Bruno AraujoCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Sandra ReisCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Samila SenaCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Maria Y T IchiharaCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Julia PescariniCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Rosemeire L FiacconeCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Leila D AmorimCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Robespierre PitaCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Marcos E BarretoCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.
Liam SmeethDepartment of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Mauricio L BarretoCentre for Data and Knowledge Integration for Health (CIDACS), Fiocruz Bahia, Parque Tecnológico da Bahia, Edf. Tecnocentro, sala 315, Rua Mundo, no 121, Salvador, 41301-110, Brazil.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundRecord 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.

methodsWe 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.

resultsOverall, 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.

conclusionCIDACS-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.

Indexed as

Datasets as TopicInformation Storage and RetrievalMedical Record LinkageAlgorithmsCohort StudiesHumansMedical Records Systems, ComputerizedAccuracyData linkageEntity resolutionIndexingInformation retrieval techniquesScalabilityScoring Search

Identifiers

PMID33167998
PMCPMC7654019

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.