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AIM in Allergy

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Artificial Intelligence in Medicine

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Abstract

Allergic disorders are highly complex and heterogeneous in the sense of pathogenesis, molecular mechanisms, diagnostic tools, and treatment success. The advent of novel systems immunology approaches unraveled new dimensions in the field of precision allergology to improve patient well-being and outcome. However, a major challenge within the field is to deconvolute the vast and diverse datasets to extract innovative and valuable information out of clinically heterogeneous patient populations. Machine-learning algorithms offer new tools to gain insights into high-dimensional omics datasets as well as clinical patient records, providing data-driven methodologies to decipher hidden features and patterns behind big data. These methods have a high translational aspect since newly identified mechanisms or endotypes can lead to innovative treatment approaches. This chapter gives a short overview of the current literature as well as an overview of the used machine-learning approaches in modern allergology. Furthermore, representative examples are given to illustrate the rationale behind the different machine-learning algorithms used in the research field of allergology.

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Authors and Affiliations

  1. Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Comprehensive Center for Pediatrics, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria

    Lukas Wisgrill

  2. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    Paulina Werner & Nanna Fyhrquist

  3. Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland

    Vittorio Fortino

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  1. Lukas Wisgrill
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  2. Paulina Werner
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  3. Vittorio Fortino
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  4. Nanna Fyhrquist
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Correspondence to Nanna Fyhrquist .

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Editors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London, London, UK

    Hutan Ashrafian

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Wisgrill, L., Werner, P., Fortino, V., Fyhrquist, N. (2022). AIM in Allergy. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_90

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  • DOI: https://doi.org/10.1007/978-3-030-64573-1_90

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