Abstract

The integration of disparate large and heterogeneous socioeconomic and clinical databases is considered essential to capture and model longitudinal and social aspects
of diseases. However, such integration is challenging: databases are stored in disparate locations, make use of different identifiers, have variable data quality, record information in bespoke purpose-specific formats and have different levels of metadata. Novel computational methods are required to integrate them and enable their statistical analyses for epidemiological research purposes. In this paper, we describe a probabilistic approach for constructing a very large population-based cohort comprised of 114 million individuals using linkages between clinical databases from the National Health System and administrative databases from governmental social programmes. We present our data integration model for creating data marts (epidemiological data) and discuss our evaluation results in controlled and uncontrolled scenarios, which demonstrate that our model and tools achieve high accuracy (minimum of 91%) in different probabilistic data integration scenarios.

Autoria: Marcos Ennes Barreto, Mauricio Barreto, Samila Sena, Rosemeire Leovigildo Fiaccone, Robespierre Dantas da Rocha Pita, Maria Yury Ichihara.

Periódico: IEEE 30th International Symposium on Computer-Based Medical Systems

Ano de publicação: 2017