Abstract: Disclosed are methods and devices of processing cytometric data. The present disclosure provides a method of processing cytometric data. The method comprises: dividing a first data matrix into a first plurality of first submatrices; encoding each of the first submatrices into one corresponding vector representation to acquire a first plurality of vector representations; and aggregating the first plurality of vector representations into a first ensemble representation. The first data matrix is indicative of a first plurality of properties of a first set of cells.

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.

Abstract: This application relates generally to a computer implemented method comprising: receiving a medical record data from a patient, wherein said record comprising a static attribute and a time dependent progression attribute; processing the time dependent progression attributes of medical record data using a trained neural network to into time-series representation, and converting the static attributes into static variables; combining the time-series representation and static variables to multiple vectors; providing a prognosis outcome by a trained classifier using said multiple vectors; wherein the neural network is trained by steps of (a) assembling a training data set comprising a retrospective collection of patients' medical record data wherein said record data comprising collected number of static attributes, time dependent progression attributes and patients' mortality and relapse outcomes; (b) processing the time dependent progression attributes of the training data set using a neural network to convert th

Abstract: This application relates generally to automated systems and methods for classifying subtypes of leukemia cells and other applications therefrom.

Abstract: This application relates generally to automated systems and methods to identify hematological abnormalities.