Neural network prediction model for dynamic survival analysis using soft labels

A computer hardware system for training a prediction model for dynamic survival analysis of a training survival dataset representing a plurality of individuals, comprising:

• a hardware processor configured to perform the following executable operations:
• initializing, for a batch of data from the training survival dataset, an estimated probability distribution for the prediction model;
• determining, for each of a plurality of censored individuals, an individual estimated probability distribution;
• constructing a soft label, for each of the plurality of censored individuals, by shifting the estimated individual probability distribution for a respective one of the plurality of censored individuals by a predetermined value;
• generating a loss by summing, for each of the plurality of censored individuals, a weighted scoring rule using the soft labels and the individual probability distributions;
• modifying the estimated probability function based upon the loss;
• and
• repeating the determining, the generating, the constructing, and the modifying until the loss is minimized, wherein
• the survival dataset includes censored data.

A computer program product, comprising:

• a computer readable storage medium having stored therein program code for training a prediction model for dynamic survival analysis of a training survival dataset representing a plurality of individuals,
• the program code, which when executed by a computer hardware system, cause the computer hardware system to perform:
• initializing, for a batch of data from the training survival dataset, an estimated probability distribution for the prediction model;
• determining, for each of a plurality of censored individuals, an individual estimated probability distribution;
• constructing a soft label, for each of the plurality of censored individuals, by shifting the estimated individual probability distribution for a respective one of the plurality of censored individuals by a predetermined value;
• generating a loss by summing, for each of the plurality of censored individuals, a weighted scoring rule using the soft labels and the individual probability distributions;
• modifying the estimated probability function based upon the loss;
• and
• repeating the determining, the generating, the constructing, and the modifying until the loss is minimized, wherein
• the survival dataset includes censored data.