Systems and methods for automated hematological abnormality detection

Abstract

This application relates generally to automated systems and associated methods for identifying hematological abnormalities. An automated system can include at least one processor that, in operation, is configured to: receive, from a flow cytometer, a flow cytometry data matrix characterizing a tube that is associated with a sample; convert the flow cytometry data matrix into a high dimensional vector; produce a single sample high dimensional vector including a concatenation of multiple high dimensional vectors associated with the sample, wherein the multiple high dimensional vectors comprise the tube high dimensional vector; assemble a training data set including multiple sample high dimensional vectors; receive, from a datastore, outcome information including respective labels associated with each of the multiple sample high dimensional vectors; and train a classifier based on the training data set and the outcome information.