Abstract: Methods, systems, and apparatus for medical diagnosis using cytometry data. One method includes receiving training flow cytometry data and obtaining a superclass-subclass relationship between a plurality of diagnosis labels in a diagnosis label space, wherein a diagnosis label represents a medical condition based on the training flow cytometry data. The method also includes, via the electronic processor, encoding cytometry domain knowledge into a plurality of soft constraints including a relationship constraint representing a correlation between a pair of diagnosis labels of the plurality of diagnosis labels and a hierarchical constraint representing the superclass-subclass relationship and training a machine learning model using a loss function including the plurality of soft constraints.