Abstract: Introduced here is an approach to improving the automatic identification of hematological diseases using computer-implemented models that are trained to rapidly distinguish between different collections of immunophenotypes that represent different disease types or disease states. Understanding the different patterns of immunophenotype collections contained in a given sample may permit a proposed diagnosis for a given hematological disease to be produced for the corresponding patient. For example, the proposed diagnoses may be output by a classification model based on the distribution of immunophenotypes across the given sample.