Preventing dataset shift from breaking machine-learning biomarkers

• URL: http://arxiv.org/abs/2107.09947v1
• Date: Wed, 21 Jul 2021 08:54:23 GMT
• Title: Preventing dataset shift from breaking machine-learning biomarkers
• Authors: J\'ero\^ome Dock\`es, Ga\"el Varoquaux (PARIETAL), Jean-Baptiste Poline
• Abstract summary: A good biomarker is one that gives reliable detection of the corresponding condition. 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.
• Score: 0.6138671548064355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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