Using Big Data in Healthcare

Using Big Data in Healthcare

Georgios Lamprinakos (National Technical University of Athens, Greece), Ioanna A. Aristeidopoulou (Intracom Telecom, Greece), Stefan Asanin (Anticimex, Greece), Andreas P. Kapsalis (National Technical University of Athens, Greece), Angelos-Christos G. Anadiotis (National Technical University of Athens, Greece), Dimitra I. Kaklamani (National Technical University of Athens, Greece) and Iakovos S. Venieris (National Technical University of Athens, Greece)
Copyright: © 2016 |Pages: 11
DOI: 10.4018/978-1-4666-9978-6.ch068
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Background

The term ʻbig dataʼ refers to high volume and complexity data, which have become extremely difficult to process using traditional techniques. One of the earliest definitions of big data was provided by Laney (2001), describing the new data growth challenges as three-dimensional (Volume, Velocity, Variety). The three-dimensional model, which stands as the most adopted definition up to date, regards big data as data that are massive in size (Volume), need to be quickly acquired and processed (Velocity), and are unstructured, loosely coupled and cannot be handled by traditional RDBMS (Variety). Another 'V' term that frequently accompanies the definition of big data is Veracity, indicating the need of processing clean and quality data, in order to achieve meaningful results from the big data use (cf. e.g., Buhl et al., 2013).

Nowadays, various sectors have transformed their way of functioning and they are collecting and exploiting large amounts of data, that may offer groundbreaking insights. Traditional services, such as weather forecasting, now require the manipulation of large amounts of data in order to provide better models, more accurate predictions, profiling and decision support. Many other paradigms exist where big data analytics can reshape well established operations. Power grids, for example, are beginning to evolve into smart grids, which are able to analyze data acquired from smart home appliances and smart sensors in order to sustain resources and energy and prevent service downtime during peak hours (Balac et al., 2013).

Key Terms in this Chapter

Big Data: Any kind of data characterized by high volume, variety and/or velocity, which are difficult to process using traditional database management tools or data processing applications.

Healthcare: The domain that is associated with the entire life cycle of humans' physical and mental problems, including prevention, diagnosis and treatment.

Big Data Analytics: The application of methods and algorithms in sets of big data in order to gain insights.

Unstructured Data: Data that has no identifiable structure, such as the text of an e-mail message.

E-Health: The provision of healthcare supported by information and communication technologies.

De-Identification: The act of removing all data that links a person to a particular piece of information.

Cloud Computing: Internet-based computing, in which large groups of remote servers are networked to allow sharing of data-processing tasks, centralized data storage, and online access to computer services or resources.

Electronic Health Record (EHR): A systematic collection of digitalized personal or massive health information.

Anonymization: The severing of links between people in a database and their records, in order to prevent the discovery of the human source of the records.

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