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Supervised learning in presence of multiple sets of noisy labels is a challenging task that is receiving increasing interest in the ever-evolving landscape of healthcare analytics. Such an issue arises when multiple annotators are tasked to manually label the same training samples, potentially giving rise to discrepancies in class assignments among the supplied labels with respect to the ground truth. Commonly, the labeling process is entrusted to a small group of domain experts, and different level of experience and subjectivity may result in noisy training labels.
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School of Medicine, Trinity College Dublin, Dublin, Ireland.
Background: Constipation is common in people with advanced cancer and is associated with significant morbidity and health economic burden, but it is often sub-optimally managed. Despite international consensus diagnostic criteria for functional and opioid-induced constipation (Rome IV diagnostic criteria), the term 'constipation' means different things to different people, impacting assessment, diagnosis and management.
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