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Perspectives on validation of clinical predictive algorithms

npj Digital Medicine, vol. 6, pp. 86

Abstract

The generalizability of predictive algorithms is of key relevance to application in clinical practice. We provide an overview of three types of generalizability, based on existing literature: temporal, geographical, and domain generalizability. These generalizability types are linked to their associated goals, methodology, and stakeholders.

Authors 6

  1. Leiden University · Leiden University Medical Center · Stanford Medicine · Stanford University

    Affiliation as printed

    Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Leiden, the Netherlands. a.a.h.de_hond@lumc.nl

    Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands. a.a.h.de_hond@lumc.nl

    Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA. a.a.h.de_hond@lumc.nl

    Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Leiden, the Netherlands

  2. Stanford Medicine · Stanford University

    Affiliation as printed

    Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA

  3. University Medical Center Utrecht

    Affiliation as printed

    Department of Digital Health, University Medical Center Utrecht, Utrecht, the Netherlands

  4. Leiden University · Leiden University Medical Center · KU Leuven

    Affiliation as printed

    Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands

    Department of Development & Regeneration, KU Leuven, Leuven, Belgium

  5. Leiden University · Leiden University Medical Center

    Affiliation as printed

    Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Leiden, the Netherlands

    Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands

  6. Stanford Medicine · Stanford University

    Affiliation as printed

    Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    Department of Epidemiology & Population Health (by courtesy), Stanford University, Stanford, CA, USA

    Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA

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References 24