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Data Standards and Terminology Including Biomedical Ontologies

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Clinical Applications of Artificial Intelligence in Real-World Data

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Abstract

Electronic health records are routinely collected as part of care and have variable data types, quality and structure. As a result, there is a need for standardization of clinical data from health records if these are to be used in software applications for data mining and/or machine learning and artificial intelligence approaches. Clinical terminologies and classification systems are available that can serve as standards to enable the harmonization of disparate data sources. In this chapter, we discuss different types of biomedical semantic standards including medically-relevant ontologies, their uses, and their limitations. We also discuss the application of semantic standards in order to provide features for use in machine learning particularly with respect to phenotypes. Finally, we discuss potential areas of improvement for the future such as covering genotypes and steps needed.

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Authors and Affiliations

  1. Institute of Health Informatics, University College London, London, UK

    Spiros Denaxas

  2. Data Science Centre, British Heart Foundation, London, UK

    Spiros Denaxas

  3. University Of Pennsylvania, Philadelphia, USA

    Christian Stoeckert

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  1. Spiros Denaxas
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Correspondence to Spiros Denaxas .

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Editors and Affiliations

  1. Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    Folkert W. Asselbergs

  2. Institute of Health Informatics, University College London, London, UK

    Spiros Denaxas

  3. Department of Data Science and Biostatistics, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands

    Daniel L. Oberski

  4. Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jason H. Moore

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Denaxas, S., Stoeckert, C. (2023). Data Standards and Terminology Including Biomedical Ontologies. In: Asselbergs, F.W., Denaxas, S., Oberski, D.L., Moore, J.H. (eds) Clinical Applications of Artificial Intelligence in Real-World Data. Springer, Cham. https://doi.org/10.1007/978-3-031-36678-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-36678-9_3

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