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Use este identificador para citar ou linkar para este item: https://repositorio.ufba.br/handle/ri/24738
Tipo: Artigo de Evento
Título: A trainable model to assess the accuracy of probabilistic record linkage
Autor(es): Pita, Robespierre
Mendonça, Everton
Reis, Sandra
Barreto, Marcos
Denaxas, Spiros
Autor(es): Pita, Robespierre
Mendonça, Everton
Reis, Sandra
Barreto, Marcos
Denaxas, Spiros
Abstract: Record linkage (RL) is the process of identifying and linking data that relates to the same physical entity across multiple heterogeneous data sources. Deterministic linkage methods rely on the presence of common uniquely identifying attributes across all sources while probabilistic approaches use non-unique attributes and calculates similarity indexes for pair wise comparisons. A key component of record linkage is accuracy assessment — the process of manually verifying and validating matched pairs to further refine linkage parameters and increase its overall effectiveness. This process however is time-consuming and impractical when applied to large administrative data sources where millions of records must be linked. Additionally, it is potentially biased as the gold standard used is often the reviewer’s intuition. In this paper, we present an approach for assessing and refining the accuracy of probabilistic linkage based on different supervised machine learning methods (decision trees, naïve Bayes, logistic regression, random forest, linear support vector machines and gradient boosted trees). We used data sets extracted from huge Brazilian socioeconomic and public health care data sources. These models were evaluated using receiver operating characteristic plots, sensitivity, specificity and positive predictive values collected from a 10-fold cross-validation method. Results show that logistic regression outperforms other classifiers and enables the creation of a generalized, very accurate model to validate linkage results.
Palavras-chave: Data linkage
Machine learning
País: Brasil
Editora / Evento / Instituição: Springer, Cham
Citação: Pita R., Mendonça E., Reis S., Barreto M., Denaxas S. (2017) A Machine Learning Trainable Model to Assess the Accuracy of Probabilistic Record Linkage. In: Bellatreche L., Chakravarthy S. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2017. Lecture Notes in Computer Science, vol 10440. Springer, Cham
Tipo de Acesso: Acesso Aberto
URI: http://repositorio.ufba.br/ri/handle/ri/24738
Data do documento: 3-Ago-2017
Aparece nas coleções:Trabalho Apresentado em Evento (PGCOMP)

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