TITLE: 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.
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.
Preventing dataset shift from breaking machine-learning biomarkers
http://arxiv.org/abs/2107.09947v1