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Big Data: Knowledge Discovery and Data Repositories

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Mental Health Informatics

Part of the book series: Health Informatics ((HI))

Abstract

“Big Data” is a concept that has been used in the last 10–15 years to describe the increasing complexity and amount of data available at scale in organizations and companies—data that often requires novel computational techniques and methods to generate knowledge. Compared to other health domains, mental health is influenced by a greater variety of factors, such as those related to mental, interpersonal, cultural, environmental, and biological phenomena. Thus, knowledge discovery in mental health research can involve a broad variety of data types and therefore data resources, including medical, behavioral, administrative, molecular, ‘omics’, environmental, financial, geographic, and social media repositories. Moreover, these varied phenomena interact in more complex ways in mental health and illness than in other domains of health so knowledge discovery must be open to this complexity. In this chapter, we outline the main underlying concepts of the “big data” paradigm and examine examples of different types of data repositories that could be used for mental health research. We also provide an example case study for developing a data repository, outlining the key considerations for designing, building, and using these types of resources.

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  1. Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Sumithra Velupillai & Katrina A. S. Davis

  2. Prometheus Research, an IQVIA Business, New Haven, CT, USA

    Leon Rozenblit

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  1. Sumithra Velupillai
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    Jessica D. Tenenbaum

  2. Six Aims for Behavioral Health, Minneapolis, MN, USA

    Piper A. Ranallo

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Velupillai, S., Davis, K.A.S., Rozenblit, L. (2021). Big Data: Knowledge Discovery and Data Repositories. In: Tenenbaum, J.D., Ranallo, P.A. (eds) Mental Health Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-70558-9_15

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