Dynamic Survival Analysis Model with Soft Labels
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Solution Overview
Problem
Existing models for dynamic survival analysis, such as the TD-PH model, are limited by their reliance on unit time intervals and the proportional hazard assumption, which do not hold in general scenarios, necessitating a more sophisticated approach.
Innovation Solution
The TD-CNLL approach combines the Temporal Difference (TD) algorithm with Censored Negative Log-Likelihood (CNLL), using soft labels constructed from estimated probability distributions of event occurrence and survival functions, allowing for flexible time intervals and relaxing the proportional hazard assumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the TD-PH model is used for dynamic survival analysis, then the model can be trained with temporal difference learning, but it is limited by reliance on unit time intervals and the proportional hazard assumption
Solution Approach 1:
The patent changes the fundamental parameters of the survival analysis model by replacing the proportional hazard assumption with direct probability distribution estimation, and by allowing flexible time intervals instead of unit time intervals. This enables the model to adapt to general scenarios while maintaining training efficiency through the TD algorithm framework.
Solution Approach 2:
The patent introduces dynamic elements by allowing the time intervals to vary flexibly rather than being fixed to unit intervals. The model dynamically adapts to different time scales and intervals, making it versatile for various real-world applications where observation intervals are irregular.
2Quantity of substance
If soft labels are constructed from estimated probability distributions, then the model achieves improved sample efficiency, but requires more sophisticated construction processes
Solution Approach 1:
The patent applies preliminary action by pre-estimating the probability distributions of event occurrence and survival functions before constructing the soft labels. This preliminary estimation step enables the model to achieve improved sample efficiency, as the soft labels are built upon these pre-computed probability distributions rather than requiring extensive retraining.
3Adaptability or versatility
If the model uses flexible time intervals instead of unit time intervals, then it can handle irregularly-sampled data, but increases model complexity
Solution Approach 1:
The patent achieves universality by designing a model that can handle both regular and irregular time intervals within a unified framework. The TD algorithm is adapted to work with flexible time intervals, allowing the same model structure to process various data sampling patterns without requiring separate specialized models for different interval types.
Data Source
AI summary
Training a prediction model for dynamic survival analysis of a training survival dataset representing a plurality of individuals includes the following operations. An estimated probability distribution for the prediction model is initialized for a batch of data from the training survival dataset. For each of a plurality of censored individuals, an individual estimated probability distribution is determined. A soft label is construed 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. A loss is generated by summing, for each of the plurality of censored individuals, a weighted scoring rule using the soft labels and the individual probability distributions. The estimated probability function is modified based upon the loss. The determining, the generating, the constructing, and the modifying are repeated until the loss is minimized. The survival dataset includes censored data.