Systems and methods for prognosis prediction of acute myeloid leukemia patients

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 the time dependent progression attributes into time-series representation; (c) processing the static attributes of the training data set into static variables; and (d) combining the time-series representation and static variables to train a classifier based on the combined time-series representation and static variables.