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Risk Stratification and Prognosis Using Predictive Modelling and Big Data Approaches

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Personalized and Precision Medicine Informatics

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

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Abstract

Predictive modeling is the application of supervised machine learning methods to risk assessment and stratification, diagnosis, prognosis and therapeutics. With increasing availability of big biomedical data, predictive modeling is increasingly applied to leverage the data for clinical medicine, public health, and biomedical research. This chapter will describe key methods and application examples in the development, validation, dissemination and deployment of clinical predictive models.

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Author information

Authors and Affiliations

  1. Department of Biomedical Informatics and of Intelligent Systems, University of Pittsburgh, Pittsburgh, PA, USA

    Shyam Visweswaran & Gregory F. Cooper

Authors
  1. Shyam Visweswaran
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  2. Gregory F. Cooper
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Corresponding author

Correspondence to Shyam Visweswaran .

Editor information

Editors and Affiliations

  1. Pharmaceutical Care & Health Systems, University of Minnesota Pharmaceutical Care & Health Systems, Minneapolis, MN, USA

    Terrence Adam

  2. Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA

    Constantin Aliferis

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Visweswaran, S., Cooper, G.F. (2020). Risk Stratification and Prognosis Using Predictive Modelling and Big Data Approaches. In: Adam, T., Aliferis, C. (eds) Personalized and Precision Medicine Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-18626-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-18626-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18625-8

  • Online ISBN: 978-3-030-18626-5

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