Stop imputing every gap: patient knowledge graphs hold their clustering quality across three missingness mechanisms
In a specialist allergy and clinical immunology cohort, representing each patient as a node in a knowledge graph and clustering graph embeddings avoided collapse under random, conditional and value-dependent missingness. The common impute-then-reduce pipeline performed worst under value-dependent missingness, because imputation conflates an untested antibody with a confirmed negative.