Skip to main content

Big Data and Machine Learning Meet the Health Sciences

  • Chapter
  • First Online:
Personalized Psychiatry

Abstract

Big data and machine learning are gaining traction in health sciences research. They might provide predictive models for both clinical practice and public health systems. Big data is a broad term used to denote volumes of large and complex measurements. Beyond genomics and other “omic” fields, big data includes administrative, molecular, clinical, environmental, sociodemographic, and even social media information. Machine learning, also known as pattern recognition, represents a range of techniques used to analyze big data by identifying patterns of interaction among features. Compared with traditional statistical methods that provide primarily average group-level results, machine learning algorithms allow predictions and stratification of clinical outcomes at the level of an individual subject. In the present chapter, we provide a concise historical perspective of some important events in health sciences and the analytical methods used to find causes and treatment of illnesses. The overall aim is to understand why big data and machine learning have recently become promising methods to define, predict, and treat illnesses, and how they can transform the way we conceptualize care in health sciences.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ives Cavalcante Passos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Passos, I.C., Ballester, P., Pinto, J.V., Mwangi, B., Kapczinski, F. (2019). Big Data and Machine Learning Meet the Health Sciences. In: Passos, I., Mwangi, B., Kapczinski, F. (eds) Personalized Psychiatry. Springer, Cham. https://doi.org/10.1007/978-3-030-03553-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03553-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03552-5

  • Online ISBN: 978-3-030-03553-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics