DISEASE PROGNOSIS PREDICTION SYSTEM BASED ON DEEP SEMI-SUPERVISED MULTI-TASK LEARNING SURVIVAL ANALYSIS

摘要

A disease prognosis prediction system based on deep semi-supervised multi-task learning survival analysis, comprising a data acquisition module, a data preprocessing module, and a prediction model construction module. The system, by using a deep neural network model as a basis, converts a survival analysis problem into a multi-task learning model composed of a semi-supervised learning problem of multi-time-sequence-point survival probability prediction; the model directly models a survival probability, does not depend on proportional risk hypothesis, can fit a time-dependent effect, and has better interpretability; it is proposed that a semi-supervised loss function and a sorting loss function are utilized to fit data, complete data and censored data are fully utilized, and traditional survival analysis problems and survival analysis problems considering competition risks can be solved; according to the model, by means of multi-task learning of multiple time sequence points, data sharing among multiple prediction tasks is achieved, mutual constraint among the multiple prediction tasks is achieved, and the generalization ability of the model is improved.