Disease prognosis prediction system based on deep semi-supervised multi-task learning survival analysis

Abstract

The invention discloses a disease prognosis prediction system based on deep semi-supervised multi-task learning survival analysis. The disease prognosis prediction system comprises a data acquisitionmodule, a data preprocessing module, a prediction model construction module and the like. On the basis of a deep neural network model, a survival analysis problem is converted 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 the survival probability, does not depend on proportional risk hypothesis, can fit a time-dependent effect, and has better interpretability; a semi-supervised loss function and a sorting loss function are utilized to fit the data, complete data and deleteddata are fully utilized, and a traditional survival analysis problem and a survival analysis problem considering competition risks can be solved; according to the model, through 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.

Description

technical field [0001] The invention belongs to the technical field of medical treatment and machine learning, and in particular relates to a disease prognosis prediction system based on deep semi-supervised multi-task learning survival analysis. Background technique [0002] Disease prognosis prediction analysis can provide clinicians with prognostic information for disease treatment, help formulate treatment plans, improve disease cure rate, improve patient prognosis and quality of life, and effectively reduce disease burden, which is of great significance for disease control and treatment. Survival analysis is a commonly used data analysis method in disease prognosis prediction, which is used to analyze and predict the time of event occurrence. In medicine, it plays a key role in defining the course of treatment, developing new drugs, preventing adverse drug reactions and improving hospital processes. Recently, with the rise of deep learning models and the improvement of...

AI Technical Summary

Problems solved by technology

[0003] Censored data is common in disease prognosis data. Censored data is not missing data, but incomplete data that can only provide prognosis information from the starting point to the censored time, but cannot provide complete information from the starting point to the occurrence of events.

Existing methods based on deep learning, or cannot make full use of censored data; or in the case of making full use of censored data, cannot effectively solve the time-dependent phenomenon of features; or the generalization ability of the model is insufficient; or the interpretability of the model poor sex

Existing methods based on multi-task learning cannot make full use of censored data

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