Exploring Hidden Patterns: A Priori Class Labels in Contrastive Learning for Phenotype Discovery

The diagnosis of complex conditions remains challenging when
biomarkers are lacking and diagnostic criteria rely on subjective clinical
judgment. We propose a novel contrastive clustering framework for phenotype
discovery, combining instance- and class-level learning with softpriors
to guide representation learning. Paired with consensus clustering,
our method guides the identification of subgroups in heterogeneous populations.
We apply this approach to a dataset of electroencephalography
and physical activity data from patients with Central Disorders of Hypersomnolence,
a clinically ambiguous spectrum that lacks biomarkers and
exhibits overlapping symptoms. To validate generalizability, we also test
the framework on an open-source dermatological image dataset characterized
by distinctly defined diagnostic categories. Our results highlight
the potential of our methodology for data-driven discoveries across a
range of clinical contexts, whilst incorporating expert clinical knowledge.