title: A health analytics semantic ETL service for obesity surveillance.
abstract: The increasingly large amount of data produced in healthcare (e.g. collected through health information systems such as electronic medical records - EMRs or collected through novel data sources such as personal health records - PHRs, social media, web resources) enable the creation of detailed records about people's health, sentiments and activities (e.g. physical activity, diet, sleep quality) that can be used in the public health area among others. However, despite the transformative potential of big data in public health surveillance there are several challenges in integrating big data. In this paper, the interoperability challenge is tackled and a semantic Extract Transform Load (ETL) service is proposed that seeks to semantically annotate big data to result into valuable data for analysis. This service is considered as part of a health analytics engine on the cloud that interacts with existing healthcare information exchange networks, like the Integrating the Healthcare Enterprise (IHE), PHRs, sensors, mobile applications, and other web resources to retrieve patient health, behavioral and daily activity data. The semantic ETL service aims at semantically integrating big data for use by analytic mechanisms. An illustrative implementation of the service on big data which is potentially relevant to human obesity, enables using appropriate analytic techniques (e.g. machine learning, text mining) that are expected to assist in identifying patterns and contributing factors (e.g. genetic background, social, environmental) for this social phenomenon and, hence, drive health policy changes and promote healthy behaviors where residents live, work, learn, shop and play.
authors.affiliation: University of Piraeus, Piraeus, Greece.
authors.forename: M
authors.initials: M
authors.lastname: Poulymenopoulou
authors.affiliation: University of Piraeus, Piraeus, Greece.
authors.forename: D
authors.initials: D
authors.lastname: Papakonstantinou
authors.affiliation: University of Piraeus, Piraeus, Greece.
authors.forename: F
authors.initials: F
authors.lastname: Malamateniou
authors.affiliation: University of Piraeus, Piraeus, Greece.
authors.forename: G
authors.initials: G
authors.lastname: Vassilacopoulos
mesh_terms: D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008498:Medical Record Linkage / Q000379:methods; D009323:Natural Language Processing; D009765:Obesity / Q000175:diagnosis* / Q000453:epidemiology*; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D015203:Reproducibility of Results; D012307:Risk Factors; D012660:Semantics; D012680:Sensitivity and Specificity