ODE-Gate RNN Hidden-State Updates for Irregular Time Series
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Solution Overview
Problem
Recurrent neural networks (RNNs) struggle to handle irregular time intervals in healthcare data, such as electronic health records, due to difficulties in capturing long-term dependencies and significant changes in hidden states when time intervals are long, and existing methods like exponential decay fail to effectively manage these changes.
Innovation Solution
The introduction of ODE-Gate RNNs, which incorporate a gate mechanism and decay mechanism to control hidden state updates, ensuring they change moderately over long time intervals, using neural ordinary differential equations (Neural ODEs) to handle irregularly-sampled time series data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If RNNs are used to process irregularly-sampled time series data with long time intervals, then the model can handle variable time intervals between observations, but the hidden states undergo significant changes that cause the model to lose long-term dependency information
Solution Approach 1:
The patent divides the time interval between observations into multiple sub-intervals by introducing intermediate time steps. This segmentation allows the model to process long time intervals in smaller chunks, preventing significant hidden state changes that would otherwise occur over long intervals and causing information loss. The intermediate steps maintain gradual transitions and preserve long-term dependency information.
Solution Approach 2:
The patent introduces an intermediary mechanism (the subdivision process with intermediate time steps) between the original hidden state and the updated hidden state. This intermediary allows for controlled, incremental updates rather than direct large jumps, maintaining information integrity while adapting to irregular time intervals.
2Stability of the object's composition
If exponential decay is applied to hidden states to manage changes over time intervals, then the model attempts to control hidden state evolution, but it fails to effectively capture both discrete observations and continuous dynamics
Solution Approach 1:
The patent replaces the static exponential decay mechanism with a dynamic approach using Neural Ordinary Differential Equations (Neural ODEs). This allows the hidden state evolution to be continuously adapted based on the actual data patterns, capturing both discrete observations and continuous dynamics between observations, rather than applying a fixed decay rate.
Solution Approach 2:
The patent substitutes the mechanical exponential decay mechanism with a learned Neural ODE system. Instead of using a predetermined decay function, the model learns the appropriate evolution dynamics from data, replacing the rigid mechanical system with a flexible, adaptive neural system that can capture complex continuous dynamics.
3Productivity
If standard RNN updates are used for each time step, then the model processes observations sequentially, but it cannot adequately handle the varying speeds of change in hidden states over different time intervals
Solution Approach 1:
The patent changes the parameters of the update mechanism by using Neural ODEs that learn the appropriate evolution rate for each time interval. Instead of using fixed update rules, the model adapts the evolution speed parameters based on the actual data characteristics, allowing it to handle varying speeds of change in hidden states across different time intervals effectively.
Data Source
AI summary
A computer-implemented method for updating hidden states in a recurrent neural network (RNN) to predict future data from multivariate time-series data with irregular time intervals is provided including inputting, for each of time steps at observations, observation data at a current time step in the multivariate time-series data to the RNN, for each of the time steps: subdividing a time interval between a previous time step and the current time step by a predetermined number, for each of subdivided time steps calculating a first element of the hidden state at a current subdivided time step using ODE-RNNs, and calculating a second element of the hidden state at the current subdivided time step using the last updated hidden state and a hidden state at the previous time step so that the last updated hidden state is decayed to be close to the hidden state at the previous time step.