Time-Window Attention LSTM for Irregular EHR Time Steps
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Solution Overview
Problem
Existing recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and its variants like T-LSTM and ATTAIN, struggle to handle time irregularity in electronic health records (EHR) due to varying time intervals between patient visits, leading to time step lags that affect prediction performance and interpretability.
Innovation Solution
The proposed Time-Window based Attention Long Short-Term Memory (TW-LSTM) network addresses this by splitting elapsed time into predetermined windows, calculating average cell states, generating attention weights, and updating cell states based on these averages to align time steps on a unified scale, thereby handling time step lags and improving prediction performance and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If LSTM is used to model sequential EHR data, then long-term dependencies can be captured, but time interval lag and time step lag cannot be handled due to constant time interval assumptions
Solution Approach 1:
The patent divides the continuous time sequence into discrete time windows of fixed length. Each time window aggregates multiple irregular time steps, transforming variable-length sequences into uniform fixed-length representations. This segmentation allows the model to handle time irregularity while preserving long-term temporal patterns.
Solution Approach 2:
The patent introduces a new time window dimension to aggregate cell states from multiple irregular time steps. By transitioning from a single time step dimension to a time window aggregation dimension, the model can accommodate variable time intervals while maintaining consistent input structures for the neural network.
2Loss of information
If attention mechanism is applied to improve interpretability, then important time periods can be identified, but time step lag makes attention interpretation difficult due to varying observation intervals
Solution Approach 1:
The attention mechanism operates on aggregated time windows rather than individual irregular time steps. By computing attention weights at the time window level, the model achieves more stable and interpretable results, as each attention weight reflects the importance of a standardized time period rather than variable-length intervals.
Solution Approach 2:
The time window aggregation serves as an intermediary layer between the irregular input sequences and the attention mechanism. This intermediary transforms variable-time-step data into fixed-length representations, enabling the attention mechanism to operate effectively and produce meaningful interpretability metrics.
3Reliability
If multiple previous cell states are used to update current cell state, then prediction performance improves, but complexity of handling time step variations increases
Solution Approach 1:
The patent groups multiple previous cell states into discrete time windows with fixed lengths. This segmentation simplifies the handling of time step variations by providing a structured framework for aggregating cell states, reducing the complexity of aligning multiple sequences with different time intervals.
Solution Approach 2:
The patent transforms the variable parameter of time step intervals into a fixed parameter of time window lengths. By changing the temporal parameter from variable intervals to fixed window sizes, the model can efficiently process multiple previous cell states without the computational complexity of continuous time alignment.
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for using a time-window based attention long short-term memory (TW-LSTM) network to analyze sequential data with time irregularity. A computer splits elapsed time into a predetermined number of time windows. The computer calculates average values of previous cell states in respective ones of the time windows and sets the average values as aggregated cell states for the respective ones of the time windows. The computer generates attention weights for the respective ones of the time windows. The computer calculates a new previous cell state, based on the aggregated cell states and the attention weights for the respective ones of the time windows. The computer updates a current cell state, based on the new previous cell state.