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Big Data and Machine Learning

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Book cover Oral Epidemiology

Part of the book series: Textbooks in Contemporary Dentistry ((TECD))

Abstract

This chapter focusses on the opportunities and challenges of big data and machine learning in relation to oral epidemiology. Big data are characterized as high-volume data that allow for smart data processing and integration of multiple data sources. They can be useful in many ways for studying the distribution and determinants of oral health. Machine learning, particularly the development of predictive models using artificial intelligence, offers great potential for the improvement of people’s oral health and care through the exploitation of big data. The knowledge derived from big data analytics can help improve oral health policy and clinical decision-making by better specification of intervention points. However, care should be applied regarding potential data quality threads such as data entry errors or non-harmonized standards for data coding. Unless based on sound theoretical frameworks and appropriate statistical methods, any type of big data analysis is prone to be corrupted by spurious correlations and fallacious inference. If used judiciously, however, big data offer vast opportunities for the creation of knowledge and (artificial) intelligence for the better promotion, protection, and management of people’s oral health.

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Author information

Authors and Affiliations

  1. Department of Dentistry – Quality and Safety of Oral Health Care, Radboud University – Radboudumc (RIHS), Nijmegen, The Netherlands

    Stefan Listl

  2. Medical Faculty, Department of Conservative Dentistry, Section for Translational Health Economics, Heidelberg University, Heidelberg, Germany

    Stefan Listl

  3. Department of Epidemiology, School of Public Health, University of São Paulo, Faculdade de Saúde Pública, São Paulo, SP, Brazil

    Alexandre D. P. Chiavegatto Filho

Authors
  1. Stefan Listl
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  2. Alexandre D. P. Chiavegatto Filho
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Corresponding author

Correspondence to Stefan Listl .

Editor information

Editors and Affiliations

  1. National Dental Research Institute, National Dental Centre Singapore, Singapore

    Marco A. Peres

  2. School of Public Health, University of São Paulo Faculty of Public Health, São Paulo, São Paulo, Brazil

    Jose Leopoldo Ferreira Antunes

  3. Dept. of Epidemiology and Public Health, University College London, London, UK

    Richard G. Watt

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Listl, S., Chiavegatto Filho, A.D.P. (2021). Big Data and Machine Learning. In: Peres, M.A., Antunes, J.L.F., Watt, R.G. (eds) Oral Epidemiology. Textbooks in Contemporary Dentistry. Springer, Cham. https://doi.org/10.1007/978-3-030-50123-5_23

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  • DOI: https://doi.org/10.1007/978-3-030-50123-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50122-8

  • Online ISBN: 978-3-030-50123-5

  • eBook Packages: MedicineMedicine (R0)

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