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Journal Articles GigaScience Year : 2021

Preventing dataset shift from breaking machine-learning biomarkers

Abstract

Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g. because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts breaks machine-learning extracted biomarkers, as well as detection and correction strategies.
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Dates and versions

hal-03293375 , version 1 (20-07-2021)

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Jérôme Dockès, Gaël Varoquaux, Jean-Baptiste Poline. Preventing dataset shift from breaking machine-learning biomarkers. GigaScience, inPress, ⟨10.1093/gigascience/giab055⟩. ⟨hal-03293375⟩
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